{"as_of":"2026-08-15T02:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3fcec04e1197ac002813d2a3619cbe129d11d2084ff299a39895d7a9f73e1a4d","coverage":[{"denominator":87,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":87,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:35:17.884833Z","state":"measured"},{"denominator":87,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":87,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.01278/citation-record","integrity":"/paper/2501.01278/integrity","json":"/paper/2501.01278/citation-record.json","paper":"/paper/2501.01278"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.296201Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.296201Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:f36cab7507f3e3626c31dc0739e6fa1e546cbe8a9e9314c01c47daf3809afd34","observation_id":"c81e0ff7-d190-4670-8901-2d262d77bc50","resolution":{"observed_at":"2026-08-10T22:35:17.296201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.302488Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.302488Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:8dc21f115e907496d36e5ed67bb86935a49113311e2b8823ec225c8859fcce37","observation_id":"e4015690-9040-4b9f-a576-5669d5f2f423","resolution":{"observed_at":"2026-08-10T22:35:17.302488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.307924Z","title":"G., Bollerslev, T., Christoffersen, P","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.307924Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:4fa6a9b524519f170a3f88bcbec359f90fbc1d73c7d1d225be136a868a93ae55","observation_id":"04dc1b97-a311-4093-a97d-db2337669bcb","resolution":{"observed_at":"2026-08-10T22:35:17.307924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.313102Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.313102Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:bd366fa7cd8b6fdd7cacf36654c7ad2fd47316f7fd9fbb883b164ac786709dc7","observation_id":"ed888c08-f481-4526-9763-254c1aa80c91","resolution":{"observed_at":"2026-08-10T22:35:17.313102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01686","last_updated":"2020-05-06T10:22:13Z","snapshot_observed_at":"2026-08-14T17:49:59.780905Z","submitted_at":"2020-05-04T17:41:59Z","title":"Neural Networks and Value at Risk","version":2},"cited_work":{"arxiv_id":"2005.01686","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.01686","snapshot_observed_at":"2026-08-10T22:35:18.247182Z","title":"Neural Networks and Value at Risk","venue":"q-fin.RM","work_id":"88ce26a7-f6ef-41fc-bcf6-b1900f173a96","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.318410Z"},"links":{"cited_paper":"/paper/2005.01686","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:202c00b69df301b5c1df737545ccc89df9dea47115208f49465483601cb8d9e6","observation_id":"30cb55eb-088e-4d38-8f25-8f6a85c013e3","resolution":{"observed_at":"2026-08-10T22:35:18.252383Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.324202Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.324202Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:290a19866bde5f1b315ce3cff323f0e1c941e54c8b4ecd454fdf65fbbf9d1e65","observation_id":"2a1f5cf9-7610-438c-8567-c76c43698ff6","resolution":{"observed_at":"2026-08-10T22:35:17.324202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.330706Z","title":null,"venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.330706Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:390a40de858d4b92e19bd868c330896b8858f10fe5687d38736287a97a86dd90","observation_id":"0d372626-2634-43f3-bb12-758bde596121","resolution":{"observed_at":"2026-08-10T22:35:17.330706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.341380Z","title":null,"venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.341380Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:fc44b868b2de5cde5decda50f4cf9ae3d5e9cc328f536c4a347efa92c25f555e","observation_id":"055501c3-9a98-41c2-95aa-497ba1c0c93f","resolution":{"observed_at":"2026-08-10T22:35:17.341380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.336279Z","title":null,"venue":null,"work_id":"7dd095fd-c46b-4cb4-8f50-18cd93016434","year":2012},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.347386Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:dd6d81592b407c5455665135d8c514d0d23881ce2ec17fa1d6e5c03b95bd290f","observation_id":"21c159fc-eca3-47d8-8a0c-163efc2f5ded","resolution":{"observed_at":"2026-08-10T22:35:19.340892Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.320839Z","title":null,"venue":null,"work_id":"ae126f64-4cbe-455d-bcdf-d858f65ea1ca","year":2017},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.353916Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:3040ea755c35da6b601974f9586df478f9888eace85020d40556bd94cdb0f8e0","observation_id":"ce600734-e1a6-4ae6-a2de-6ad88ef5e26b","resolution":{"observed_at":"2026-08-10T22:35:19.325676Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.305999Z","title":null,"venue":null,"work_id":"16191383-3cc8-4865-9e6c-ed0de64cf892","year":2019},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.359833Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a151e060195ff69d7e84a2bfc62e5b59375fb412934b7d1b80eba2c1a8610f9a","observation_id":"c51af161-e8df-46dc-a9f7-d9cb6edc001c","resolution":{"observed_at":"2026-08-10T22:35:19.310782Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.290748Z","title":null,"venue":null,"work_id":"75d08d43-5486-4edb-ad87-c6465ad509f2","year":2021},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.365450Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:52c7e25229bd6fcfa37d3f52b08b2981f51628f9a0726933939ebfef39144c5e","observation_id":"6adb13a4-fffd-46e3-a860-5f45929f7136","resolution":{"observed_at":"2026-08-10T22:35:19.295693Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.275031Z","title":"and Chlebus, M","venue":null,"work_id":"0200e5ef-b9ef-45c1-a38b-fd3c2d5066e5","year":2023},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.375330Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:3c4c76514d59bbb2b95bfc11bccf7f829f534d32bae29fd7807a389b0c4a8f52","observation_id":"bc7325fb-c602-4d89-85de-c8e4dd4203a1","resolution":{"observed_at":"2026-08-10T22:35:19.279763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.260125Z","title":"Y., Lo, A","venue":null,"work_id":"a0c7671f-098e-4c48-9208-2d4e94a782ef","year":1998},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.381568Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:d8ae9a74ec9e04244b7e9d898eaf04211518d4de04a9c35b6ae871854232c1f3","observation_id":"36597e08-4b39-4186-bebb-726fe55b324f","resolution":{"observed_at":"2026-08-10T22:35:19.264972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.244841Z","title":null,"venue":null,"work_id":"9a18a42d-4e51-4ffd-ae12-324a9a733f91","year":2005},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.386756Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:dfd6783c11d7941a0a225f5c5b3d3ece948b6dee004f41cfd38bb2cd6d9de97f","observation_id":"ed032487-9153-4e7b-aa08-e19cde4b5071","resolution":{"observed_at":"2026-08-10T22:35:19.249623Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.228946Z","title":"D., Teoh, E","venue":null,"work_id":"b06a6eac-f6d4-40a5-807e-7ff296ac8d2e","year":2018},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.393499Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:fcc3eade998ebe7de98dce1b5a6a3e3a4f5a63923b6cba232c2284bd0cd37de9","observation_id":"96dbae40-b394-4cea-8464-8623ab6a8936","resolution":{"observed_at":"2026-08-10T22:35:19.234092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.212114Z","title":null,"venue":null,"work_id":"84add0fa-c27e-4aba-820e-7dd49b6be231","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.399361Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:5fb1f9c5177f96aa03f1b0047a0394e00c9852fbd7f5be8f5eec32a227c8a29d","observation_id":"f459a0d0-f9d8-4d4a-adb9-95934a223590","resolution":{"observed_at":"2026-08-10T22:35:19.217727Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.195551Z","title":"B., and LeCun, Y","venue":null,"work_id":"3b4e4548-7ceb-4f36-b9dc-a279bd76426b","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.405120Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:83359d6a87c32eed7a8f14801cabe55b59d284826fcc09813d7558b64a538dc2","observation_id":"e9643b4c-56d8-4d7a-9560-b88bca33c2f6","resolution":{"observed_at":"2026-08-10T22:35:19.200516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.179747Z","title":null,"venue":null,"work_id":"1001a0f7-921c-484e-84b6-df7501c81bcf","year":2008},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.410893Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:f688875e4058e0edc15950f4938cd34e0e905d3dc125961578986a5b416c724c","observation_id":"4ef39c8f-ebbb-4d20-a580-8b4bc8ff2015","resolution":{"observed_at":"2026-08-10T22:35:19.184618Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.164173Z","title":null,"venue":null,"work_id":"16a7d3bc-1058-4ae9-b577-01dd408cf4ae","year":1998},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.419479Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:3f33e1340e60eb8ac210efcb766b7790347711632f017df86aa87b0ce6f41641","observation_id":"dfae0375-2ab9-4d37-9ee7-3e7b5822afdc","resolution":{"observed_at":"2026-08-10T22:35:19.168964Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07289","last_updated":"2016-02-22T07:02:58Z","snapshot_observed_at":"2026-08-14T22:21:43.095256Z","submitted_at":"2015-11-23T15:58:05Z","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07289","snapshot_observed_at":"2026-08-10T22:35:17.424951Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.424951Z"},"links":{"cited_paper":"/paper/1511.07289","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:c89558237ae3c362fcd32745a61470785052c9614a4bf17595b3f047f8eace58","observation_id":"575204cf-6055-45fa-909c-01e57b484c4f","resolution":{"observed_at":"2026-08-10T22:35:17.424951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.147585Z","title":null,"venue":null,"work_id":"7dcf5845-3f42-49ea-bab8-be1af7155b4e","year":2007},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.430334Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:5040b2227069db861bc64a9f9bc87d11993da75185fc7f1b3ee206aad3a04acd","observation_id":"97c7adb0-687c-4ebf-8189-7ae3c626af35","resolution":{"observed_at":"2026-08-10T22:35:19.152836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.130900Z","title":"G., Vermunt, J","venue":null,"work_id":"1272e770-9e09-41a0-9bb1-1ee13abee68d","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.435355Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:96d093e1418a4d460ad294a065f2c30affed66685eb478ad76e074e862d1aedd","observation_id":"1c2b899b-ecca-44a0-9185-91a16aaa1ada","resolution":{"observed_at":"2026-08-10T22:35:19.135864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.10909","last_updated":"2024-10-27T06:56:09Z","snapshot_observed_at":"2026-08-13T14:21:50.314308Z","submitted_at":"2022-09-22T10:32:08Z","title":"Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems","version":3},"cited_work":{"arxiv_id":"2209.10909","doi":null,"metadata_source":"pith","pith_arxiv_id":"2209.10909","snapshot_observed_at":"2026-08-10T22:35:18.209215Z","title":"Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems","venue":"cs.LG","work_id":"c974f8f4-f598-4174-a94e-0fe58c639c90","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.443271Z"},"links":{"cited_paper":"/paper/2209.10909","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:32ce132cb24ba150262fd2e2bc7470018c695e841cb9447bdd873ae076997470","observation_id":"9dd9c0f7-89ed-4bf3-8e9b-3dcbc662a973","resolution":{"observed_at":"2026-08-10T22:35:18.214077Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.07859","last_updated":"2019-01-23T13:06:09Z","snapshot_observed_at":"2026-08-14T17:27:06.389025Z","submitted_at":"2019-01-23T13:06:09Z","title":"How do Mixture Density RNNs Predict the Future?","version":1},"cited_work":{"arxiv_id":"1901.07859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.07859","snapshot_observed_at":"2026-08-10T22:35:18.184478Z","title":"How do Mixture Density RNNs Predict the Future?","venue":"cs.LG","work_id":"5b45ea4f-4d55-4b79-b634-0bd12a19fd46","year":2019},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.450540Z"},"links":{"cited_paper":"/paper/1901.07859","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:9dcc5132fc5318ae4f01e179f55f96a1b32cc3f19f372ce2c57a40c38b4ea7fb","observation_id":"934bab0a-1cbd-492c-9ffb-210f8524160b","resolution":{"observed_at":"2026-08-10T22:35:18.190537Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.459998Z","title":null,"venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.459998Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:303840167da24b5caca55a208afc3516b16047994c46efccd7c74dba8e1268d2","observation_id":"78dd1fc7-8af2-433d-b3fe-88567ea8aaad","resolution":{"observed_at":"2026-08-10T22:35:17.459998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.103416Z","title":null,"venue":null,"work_id":"c9cba9dc-7396-45e9-824d-d5011eb24a8c","year":1994},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.465023Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:2aabfbb8f189f0cced7755ee8a730b2fd864e52d40a06ea1faf904f6a62ac916","observation_id":"32da639f-14cf-43c7-970d-bc93a33b5105","resolution":{"observed_at":"2026-08-10T22:35:19.108608Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.15111","last_updated":"2020-10-28T17:53:57Z","snapshot_observed_at":"2026-08-12T16:19:54.025668Z","submitted_at":"2020-10-28T17:53:57Z","title":"Evaluating data augmentation for financial time series classification","version":1},"cited_work":{"arxiv_id":"2010.15111","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.15111","snapshot_observed_at":"2026-08-10T22:35:18.155578Z","title":"Evaluating data augmentation for financial time series classification","venue":"q-fin.ST","work_id":"3a6e7529-d8c1-4f94-a6f1-1c5d882eb969","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.472876Z"},"links":{"cited_paper":"/paper/2010.15111","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:f88b92e31b1e90c6815b57955ba7807ff6cee6b5e00221d53fcee77b03a514a3","observation_id":"ad4f633f-7f82-463e-a223-b71d5b64fb60","resolution":{"observed_at":"2026-08-10T22:35:18.164838Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.085261Z","title":null,"venue":null,"work_id":"7366d63d-ebb4-4adf-93a2-4058da3ab261","year":2000},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.479278Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:b97dded5f08be3ad5474f41c633cf355678dbc192a6817c63c4e5ef03b492d37","observation_id":"becbe9e1-7ec6-4a8e-8369-1f8cb5772f4c","resolution":{"observed_at":"2026-08-10T22:35:19.090861Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.066273Z","title":"and Bengio, Y","venue":null,"work_id":"dbd1f0a1-39da-45cd-9a8d-7aab2eb216d0","year":2010},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.484779Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:2a94ea0a8590c856580103594e19e055fb9bb51fd89316e21b139b864e42a1de","observation_id":"d7657776-493e-4f10-8b98-e7e42824220b","resolution":{"observed_at":"2026-08-10T22:35:19.072007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.049242Z","title":null,"venue":null,"work_id":"d7d8d1ac-29a3-42cf-9edf-75070b51ca61","year":2016},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.490497Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a8b513f176be78629aa775a88fabd5e55f1ff34fe0a8162a300fea66cb0eb37b","observation_id":"7564a6ff-09e1-44d4-bfda-5fb1eef328cf","resolution":{"observed_at":"2026-08-10T22:35:19.055362Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.0850","last_updated":"2014-06-05T16:04:02Z","snapshot_observed_at":"2026-08-15T00:07:53.077276Z","submitted_at":"2013-08-04T21:04:36Z","title":"Generating Sequences With Recurrent Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.0850","snapshot_observed_at":"2026-08-10T22:35:17.497028Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.497028Z"},"links":{"cited_paper":"/paper/1308.0850","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:20e753bc594ddd35eab91f3439f3e768d4745175fb49b0039249fe56e97f1e06","observation_id":"fe148da3-98ab-48b7-a00c-67e833b5ea82","resolution":{"observed_at":"2026-08-10T22:35:17.497028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.505833Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.505833Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:5a2369bdbe9828d38fb8c3c3dce4e1617b4a0daa1528407e4b7c588d1c64e27b","observation_id":"f8e3108f-7c5c-4d21-9103-15561855d585","resolution":{"observed_at":"2026-08-10T22:35:17.505833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.021246Z","title":null,"venue":null,"work_id":"63a00dad-baba-44a4-bb3d-d9475695d3cd","year":2013},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.512113Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:f18d4de6f1754d37789f999c620cf90ad148ff2abd9350683aaec5023195e9a4","observation_id":"c9356cf5-b9e5-4c19-902b-8b4138abaad0","resolution":{"observed_at":"2026-08-10T22:35:19.026800Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:19.004223Z","title":null,"venue":null,"work_id":"253c33ed-1dab-444b-9b83-42b28ed2b88f","year":2005},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.517809Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:5da2331fb444012352f69dd9edf04f58b5e2d430af490f5ec3dadb04ed80c04f","observation_id":"98ee111a-273a-41b5-ba4b-ac8a505778d9","resolution":{"observed_at":"2026-08-10T22:35:19.009723Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:17.523999Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.523999Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:44f6423fd3d95a9800fc2e8d43f91ba53ae457f2d729ca002b51309d2c6098b5","observation_id":"5e29f677-591b-4599-8fa2-e17abf09a532","resolution":{"observed_at":"2026-08-10T22:35:17.523999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.08924","last_updated":"2022-05-19T07:26:12Z","snapshot_observed_at":"2026-08-13T15:42:04.314920Z","submitted_at":"2022-05-18T13:39:27Z","title":"Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots","version":2},"cited_work":{"arxiv_id":"2205.08924","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.08924","snapshot_observed_at":"2026-08-10T22:35:18.100999Z","title":"Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots","venue":"cs.CV","work_id":"7e8c8bbe-5f5c-4834-b98f-0cd6a3c7d1d4","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.531743Z"},"links":{"cited_paper":"/paper/2205.08924","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:58945d7630c97ac1333c94b1ee956baad797751c08573ddeea03e9041c7cf8be","observation_id":"76474b97-36f1-4cdd-9894-adc98b3324bb","resolution":{"observed_at":"2026-08-10T22:35:18.109627Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.976151Z","title":null,"venue":null,"work_id":"8b0f91d0-ae65-4693-b355-0e462d09e0f8","year":2002},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.538605Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:265378a88624466b614ff854592b2e50f889c5cdff3d65dfd9efdd578a47a92a","observation_id":"40a77ac9-bbb5-46d7-b225-97d1ee334e47","resolution":{"observed_at":"2026-08-10T22:35:18.981151Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.959577Z","title":"and Raghuram, S","venue":null,"work_id":"357010a7-705b-4c6d-ab88-fce5e8c75cca","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.545552Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:9cfb7b9ae86407d137378b026d207a89280c74f512b5ec5d3add804b28b6ed34","observation_id":"94f0fa21-859e-4f32-bc88-8548c39b9cc3","resolution":{"observed_at":"2026-08-10T22:35:18.965040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.943646Z","title":null,"venue":null,"work_id":"94ab712e-c90a-4d49-ae41-e1adf75bbecc","year":1992},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.551367Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:cb4177a6ad2fb1703d1015349e276bf1d958d30872877acdb322c11ba9c8c952","observation_id":"b9f882e7-2353-442d-aab0-384975bf0e15","resolution":{"observed_at":"2026-08-10T22:35:18.948852Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.928078Z","title":"and Saphir, D","venue":null,"work_id":"d5299a84-245d-4af8-9414-abd29aa9cbd2","year":2021},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.557536Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:fa58ffbc1e0a081d5948e94e071c24bfc7f58bad9a8138441145d015be5c1cd3","observation_id":"725939bc-6d88-4225-bc54-7208243fd430","resolution":{"observed_at":"2026-08-10T22:35:18.932910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.912449Z","title":null,"venue":null,"work_id":"3d72afbf-5f85-43fc-be7c-49902af19f60","year":2016},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.562527Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:d9138c96be27a8be8f9a1d1e1680a08210bf97441b54f8dacc945bdf349062c4","observation_id":"d94d2d50-dd81-4777-840a-55afd065d3d8","resolution":{"observed_at":"2026-08-10T22:35:18.917352Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-10T22:35:17.567816Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.567816Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a116c3979aca66143cdfebbd0567ee7eb9d0b12111f7ea91ffa629c2e3f73a68","observation_id":"ccaf31a3-f9dd-49fe-883e-1bafad1bbe24","resolution":{"observed_at":"2026-08-10T22:35:17.567816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.08863","last_updated":"2017-05-02T22:43:10Z","snapshot_observed_at":"2026-08-14T21:04:05.135230Z","submitted_at":"2017-04-28T09:57:52Z","title":"On weight initialization in deep neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.08863","snapshot_observed_at":"2026-08-10T22:35:17.573819Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.573819Z"},"links":{"cited_paper":"/paper/1704.08863","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:b9ebf2fdb2e43ea16e0945673d1b03490c5aa5ceb98c4552caffa389fde6a412","observation_id":"c310d262-60e7-472c-b658-b5ebd47fa836","resolution":{"observed_at":"2026-08-10T22:35:17.573819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.897068Z","title":null,"venue":null,"work_id":"f3c9582d-9765-4d7c-9f15-450321f72c2e","year":1995},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.580044Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:47e09ea46bfe927965c8fbccdf4ff6b19605949cf784cfa84ea2e0bb9b3dce48","observation_id":"50e0eb01-3ae8-4bb3-a147-0fd0b38060c9","resolution":{"observed_at":"2026-08-10T22:35:18.901813Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.881376Z","title":null,"venue":null,"work_id":"7e4d40a6-a8c0-434f-a135-219462f274fe","year":2021},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.586848Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:b734cb83a33077c8674fe7e691b7d26c9ddfdb13f8aa6869c1f9972623ae7a41","observation_id":"6b1edd6e-6d75-42b0-9f56-e5a9e0e10ca8","resolution":{"observed_at":"2026-08-10T22:35:18.886512Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.06733","last_updated":"2020-10-21T19:19:02Z","snapshot_observed_at":"2026-08-14T17:01:32.909648Z","submitted_at":"2019-03-15T18:23:55Z","title":"Dying ReLU and Initialization: Theory and Numerical Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.06733","snapshot_observed_at":"2026-08-10T22:35:17.597382Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.597382Z"},"links":{"cited_paper":"/paper/1903.06733","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:e740a57852a839a8bf0824c003eaefbe00f45ee842a2468072d8b0b368f021a3","observation_id":"06227ee4-62f2-434a-a1d9-91c9bb513069","resolution":{"observed_at":"2026-08-10T22:35:17.597382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.865618Z","title":"M., McCurdy, T","venue":null,"work_id":"d38dc396-f131-48c3-b5ee-1735b136ce7d","year":2012},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.606644Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:6780517c9ade8a600f83d27bc2f18d5c0a03179da509c4c2ef5d4de1627ee8e8","observation_id":"3d9c8aed-83a7-4c22-b04e-8dce577e3b00","resolution":{"observed_at":"2026-08-10T22:35:18.870470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.848432Z","title":"and Ter \\\"a svirta, T","venue":null,"work_id":"ae91ea66-5889-435f-996b-4af0e3451d5c","year":2010},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.619562Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:7a9eefef670510a5c045820a289a6639bc11b753c596db913c85e1afb8217a22","observation_id":"47ec6107-8e8c-440f-92ba-3124bc64e06c","resolution":{"observed_at":"2026-08-10T22:35:18.854236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.831768Z","title":"and Engle, R","venue":null,"work_id":"7c283c06-9401-4dae-8162-2dcbb9b6392a","year":2001},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.624732Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a404ab5aa6f7eb0691df2aa033286103d7ecbac10794766b442e470c5e02d05e","observation_id":"a690b51b-034e-4967-a5d1-a16f8bee9ab5","resolution":{"observed_at":"2026-08-10T22:35:18.836880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.815959Z","title":null,"venue":null,"work_id":"ac14915a-32fa-4869-8c46-13c3e04aaddf","year":2018},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.630398Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:7b0241cdf8417940eb04f419ca8d3fcd9b4c0f2b541e12f6596e6cc9d2617434","observation_id":"b3d2cf0e-399a-4605-9410-92db35f80f14","resolution":{"observed_at":"2026-08-10T22:35:18.821158Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.800957Z","title":"A., and Robles-Kelly, A","venue":null,"work_id":"f0a380df-f1a6-4754-9242-478c65430e3c","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.637089Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:785086e773539443a8eeb2909a20d82e6b6b47c1727c1c09bf20786af46feb7f","observation_id":"f8e26367-f365-40fd-81d0-04564a163982","resolution":{"observed_at":"2026-08-10T22:35:18.805798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.785351Z","title":"J., Frey, R., and Embrechts, P","venue":null,"work_id":"0bf56aaf-372c-4fe0-bde4-8876619051c0","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.642502Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:58559d938f335a803f98606c7499e9981348249640202f6fe248cbea37c45f0a","observation_id":"b64d5e05-3a17-4da6-a957-e5b9efa14bcb","resolution":{"observed_at":"2026-08-10T22:35:18.790221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06422","last_updated":"2016-02-19T14:37:10Z","snapshot_observed_at":"2026-08-14T22:22:07.454491Z","submitted_at":"2015-11-19T22:19:15Z","title":"All you need is a good init","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06422","snapshot_observed_at":"2026-08-10T22:35:17.650164Z","title":"and Matas, J","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.650164Z"},"links":{"cited_paper":"/paper/1511.06422","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:9c519c9070738bf8d93fb6a601cea4bf5a6bf48eb221f62cc03927f23466df85","observation_id":"d5cd9e4d-b1fa-44f0-8734-423ddd82fc28","resolution":{"observed_at":"2026-08-10T22:35:17.650164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.769779Z","title":null,"venue":null,"work_id":"65d88fa4-d80e-4e2c-9fc0-58b69fb6926f","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.656146Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:44af6de2260322be5045f00661bd3e4ae38c699a8416e460ba2b0e7457422b37","observation_id":"8d34928f-521d-46a0-a8d8-a2c576e95fae","resolution":{"observed_at":"2026-08-10T22:35:18.774596Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.754142Z","title":"G., and Charles, W","venue":null,"work_id":"12ac154a-a614-4094-a95d-f1172a88d61f","year":2014},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.664276Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:fba42abd82fa1d09dee9a397d9a1001160ecc712fa6eada97a6a684636687bbf","observation_id":"ae8c6cfa-c0ea-4d7b-95d7-44a8367eb16b","resolution":{"observed_at":"2026-08-10T22:35:18.759346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.738137Z","title":"V., Bartakke, P","venue":null,"work_id":"c196892c-2156-4f53-9d3f-833cd0afa335","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.671037Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:c9d0fd7db23c1de94d6e9d20919740f5ecf710b411c8c64c100d7f1870f2ecfb","observation_id":"64bc452f-8f5e-440a-a5d1-7b561c1b4210","resolution":{"observed_at":"2026-08-10T22:35:18.742945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.722607Z","title":null,"venue":null,"work_id":"7a7aaf53-6b85-461f-8d90-217a3df4e864","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.678560Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:caa7832145bbc918160f3c99ed412d08ae5e1e6add9775f361276a840e4100e3","observation_id":"1e3bc724-098c-4f39-8d52-fe3e9c6b145f","resolution":{"observed_at":"2026-08-10T22:35:18.727734Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.707137Z","title":null,"venue":null,"work_id":"43d7d74c-6b64-4bc2-9eff-96aa3d562e50","year":2009},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.686539Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:e42b88f86f115ac7afe569140ddb3d56bf23947e12a4ff9121d79065623888c0","observation_id":"f8a1425a-136f-4e61-8a65-ed692889a8cd","resolution":{"observed_at":"2026-08-10T22:35:18.712102Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10539","last_updated":"2022-07-21T15:26:07Z","snapshot_observed_at":"2026-08-13T14:59:23.073267Z","submitted_at":"2022-07-21T15:26:07Z","title":"Estimating value at risk: LSTM vs. GARCH","version":1},"cited_work":{"arxiv_id":"2207.10539","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.10539","snapshot_observed_at":"2026-08-10T22:35:17.985144Z","title":"Estimating value at risk: LSTM vs. GARCH","venue":"q-fin.RM","work_id":"db0e2cb5-6cc9-444f-b1e0-c5f695c6d4d7","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.693174Z"},"links":{"cited_paper":"/paper/2207.10539","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:51a3e9389615ddb318c4a37ec56643d81ffd6e43493fb41162b398da6807d209","observation_id":"5c34401e-c67b-49ce-9d6f-e03582b70cc8","resolution":{"observed_at":"2026-08-10T22:35:17.991623Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.690052Z","title":null,"venue":null,"work_id":"aaf0f9c1-e265-40cf-9b8a-a27f138d4502","year":2013},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.698811Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:29fcd370743a76492bc42439d8b99d74beca743942c0c1b57b12c48b3de6139a","observation_id":"26fb3324-10f8-41c5-abc9-aa53426b9d91","resolution":{"observed_at":"2026-08-10T22:35:18.696396Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.672263Z","title":"and Smith, D","venue":null,"work_id":"53335aa2-12fe-41b6-8f9d-92d7ef1ce5e2","year":2010},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.704835Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:edf72f5fc3bbf0ee63ed1ad0426f7d59b219d6a1618e4915fd9a41ddd8b0544f","observation_id":"67195cd9-96f3-4a2c-8d03-7b01f2c7b3ec","resolution":{"observed_at":"2026-08-10T22:35:18.677285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.655985Z","title":"R: A Language and Environment for Statistical Computing","venue":null,"work_id":"ab2313df-e498-452f-94de-ad37d6f749db","year":2021},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.717284Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:81f9b493847c39fd2381d5023cf57bf124e2a95301bd9d36ec35c5ed119ce2bc","observation_id":"55ca85d3-05ec-454c-a181-675a1d4d960e","resolution":{"observed_at":"2026-08-10T22:35:18.661019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.639627Z","title":"J., Jain, A","venue":null,"work_id":"4a0e7390-8913-4d2b-8349-3aaf0905ed24","year":1991},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.723753Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:1c6070d1d1fb7e532753f6b72f24496baec7ae7dcde0ba7789a96ba91a64d757","observation_id":"06dd861d-db4c-469d-bb4a-2fc6b2f10504","resolution":{"observed_at":"2026-08-10T22:35:18.644688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.624048Z","title":null,"venue":null,"work_id":"f4d3180e-12ce-49b1-bd2e-fb61ef21681b","year":2009},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.730142Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:d431623a186bbc09d4f76f02ae063ab4861556e0545b7125f86fdfbf50f2ce8f","observation_id":"9ff282e5-fd4c-4903-aaa0-88230e57135e","resolution":{"observed_at":"2026-08-10T22:35:18.629191Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.608368Z","title":null,"venue":null,"work_id":"0fbbb0fa-1469-47b9-9e3d-1561502f375d","year":1958},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.735911Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:e6b532e6704f5e7472b23cbe351f09942a7262d1bc39b86bf97f3e5573e6aca3","observation_id":"0fb5ac17-4bb1-486d-a859-711caffcda4e","resolution":{"observed_at":"2026-08-10T22:35:18.613330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.590806Z","title":"P., Jain, N., Singh, A., and Jain, G","venue":null,"work_id":"4c7b141d-0611-4636-82f7-e4a092d84bd9","year":2017},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.741434Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:b3cf12d9d718229ecd776052284f31abed436038c7719d0b59d17ac87b5c0934","observation_id":"69c04dd8-abdb-4bca-8bb4-e4b33ecd024c","resolution":{"observed_at":"2026-08-10T22:35:18.595954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.573675Z","title":null,"venue":null,"work_id":"436010d8-61f1-48f6-a165-7e1349a7bf5c","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.747186Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:aaad34d182a3b9d7657ba5ee7885d137bca215f3ef0aaa2f1237eae578a68e27","observation_id":"cf58185a-76cb-4566-bdb5-1c78d9475a75","resolution":{"observed_at":"2026-08-10T22:35:18.580006Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.556887Z","title":null,"venue":null,"work_id":"104a209f-36b6-40b4-97b2-c8b1aa592970","year":1997},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.752168Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:c3db69ddea33e595f63253e48cca92b1b185307aecdfa4f5dcb4bf99c8772d96","observation_id":"d70cabc9-0ffe-4f26-b5ba-4cc5295eeb0a","resolution":{"observed_at":"2026-08-10T22:35:18.563076Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.542638Z","title":null,"venue":null,"work_id":"664e5ef3-fd64-4b83-ab95-11a39c64ee73","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.768245Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:bf7564aa5e6410a14267fbdcbd74639eb81157f4f55ea24fdfb724b18734b69f","observation_id":"f3779186-4f13-4f66-b5b1-8d5757c47258","resolution":{"observed_at":"2026-08-10T22:35:18.547273Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.526828Z","title":null,"venue":null,"work_id":"5f22eaf8-8a42-4ed8-aaa9-c9e6e67c6206","year":2011},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.774370Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:7510b643601928b53913c2ec8e12221db8cecf6cbe6493576a50529afc6aac60","observation_id":"bb779d4f-a804-422d-b3d2-31eba417621a","resolution":{"observed_at":"2026-08-10T22:35:18.531932Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.510148Z","title":null,"venue":null,"work_id":"a9e5ae17-1bf8-4f79-b146-42c3af965d3f","year":2012},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.782905Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:d5527e03900a989e090d295d2c5454e863192758ca962246a7be0359601c7600","observation_id":"a7183546-fae1-4649-9c86-db55037a3561","resolution":{"observed_at":"2026-08-10T22:35:18.515257Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.494488Z","title":null,"venue":null,"work_id":"f9c37d74-3409-41e0-a9b3-df383a9845ce","year":2017},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.790401Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:e61a67de2e380197f420466457914dbcb6cfcdc62f8b142ba1b14f0fb87deb13","observation_id":"d4713634-0bf7-4970-add1-1d1061787b6c","resolution":{"observed_at":"2026-08-10T22:35:18.499368Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.478788Z","title":null,"venue":null,"work_id":"cfa9c219-87d1-49cd-9f6e-eeccef996d37","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.797704Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:785664eb34a0450717df46bcc05fca0caebd7c70fe9c917cd5261f357bbc24b7","observation_id":"4474f9dd-c3ba-4f94-bb28-039efcb7ab36","resolution":{"observed_at":"2026-08-10T22:35:18.483708Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01342","last_updated":"2023-03-03T04:34:17Z","snapshot_observed_at":"2026-08-13T15:30:49.663584Z","submitted_at":"2022-06-02T23:52:35Z","title":"Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning","version":3},"cited_work":{"arxiv_id":"2206.01342","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.01342","snapshot_observed_at":"2026-08-10T22:35:17.953138Z","title":"Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning","venue":"cs.LG","work_id":"af57d606-610b-4b87-8e0b-5561cc26f968","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.804283Z"},"links":{"cited_paper":"/paper/2206.01342","citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:6a78c6639534bf845eed261daa52d9fcf21f6509ed521d01c4df78006b676c98","observation_id":"4f46bed9-daaf-4e41-9cd3-b42ec2e3707e","resolution":{"observed_at":"2026-08-10T22:35:17.962692Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.461076Z","title":"and Hornik, K","venue":null,"work_id":"bad5cbe0-2f32-40e5-8cab-b062d59cc6f0","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.811970Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:ab4aba6bd2e9b09e32d5d554c48ebae8a5c16dda4ceabf649ff9e18b0a59aa42","observation_id":"3b49243b-8532-420d-b85a-f20a1d261dcd","resolution":{"observed_at":"2026-08-10T22:35:18.465881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.445581Z","title":null,"venue":null,"work_id":"051a6fca-8e38-49f6-9b35-7becaf7f3f4c","year":2017},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.817772Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a1e38d95434c427110a8175ab68e858fa0add584f5ecc16743af256c4d8ab373","observation_id":"6ba65035-f059-4d6e-ad03-55fcf33234f7","resolution":{"observed_at":"2026-08-10T22:35:18.450758Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.429015Z","title":null,"venue":null,"work_id":"ef48529c-8bfa-4bbd-b0bf-6db6e3abb420","year":2014},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.823402Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:16ab8a0726d02f1144f0c9fbbe5c37b01ebcc81bb322db4b20ab7fa373d8fa34","observation_id":"3ee9a096-5904-463f-804b-d611997dbf06","resolution":{"observed_at":"2026-08-10T22:35:18.434748Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.406196Z","title":null,"venue":null,"work_id":"cd71d3d9-6dc6-4adf-9dd2-17c22a0fd008","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.829080Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:5dec3e77aec5a04674d6b8daff5bb92b133cbcbd3de236341928b91820a16e64","observation_id":"07482284-1af9-4b05-aebe-64707c226fff","resolution":{"observed_at":"2026-08-10T22:35:18.411142Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.389582Z","title":"and Li, S","venue":null,"work_id":"03511038-c7fd-4146-8195-b65a1b9af2fd","year":2016},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.836150Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:7ceff5f4c830925cea406294f1a599ad1e9a7cedd23be5df09727a0f2b01c6a9","observation_id":"f7980a0c-a42d-4712-9d21-6d9bf727632c","resolution":{"observed_at":"2026-08-10T22:35:18.395220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.364750Z","title":"and Jamil, N","venue":null,"work_id":"d3e89308-fb3d-47da-a101-bd6cbe9ad80f","year":2020},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.843546Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:69b0009d5f56154f665360e8e2e2e96107011cf7a3d8193410ce93b9e92d8bf3","observation_id":"2f66a88b-c07e-4413-8765-cc7bae5e953a","resolution":{"observed_at":"2026-08-10T22:35:18.370022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.348579Z","title":"and Drake, F","venue":null,"work_id":"91955132-de20-469d-af40-735bf57c39ce","year":2009},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.849600Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:0ef0e808ba665d104a34753f7d84032b19b69b1cdcc219a5b52e147029ebed93","observation_id":"1c5312c4-ebae-4b4d-bd73-6571c2d6be3d","resolution":{"observed_at":"2026-08-10T22:35:18.353598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.330912Z","title":null,"venue":null,"work_id":"e6a2ecfa-2f83-4a21-a85f-a12f88a3eda9","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.854609Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:a0ea280f75c9c4180ed2fb169f7950836574fd6d86bdf57f328e792cb59dd085","observation_id":"413f28b3-ebde-471a-9e58-79dae7bd8c3f","resolution":{"observed_at":"2026-08-10T22:35:18.336439Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.313899Z","title":"S., and Karray, F","venue":null,"work_id":"521dfe2b-7fdb-4c4d-99a5-83cd346172b8","year":1998},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.859939Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:76e4ae126867d8d04001140bc33d56947852893acc76f528354fb9c443aa82ea","observation_id":"8a5bbc45-fa7b-45d2-8f8f-57e1fbdd0a41","resolution":{"observed_at":"2026-08-10T22:35:18.319008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.297056Z","title":"Coronavirus disease (covid-19) pandemic","venue":null,"work_id":"f9054d6c-278c-4367-ad2b-1ce4c376e9e5","year":2023},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.864721Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:9a016bb8a3adc293365c0fa4b7368b660fde04789909b88c887db216b6877981","observation_id":"8d6d35a0-e811-45eb-8e56-ad6d0bfae845","resolution":{"observed_at":"2026-08-10T22:35:18.302696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.280099Z","title":null,"venue":null,"work_id":"6926dfa8-ef18-4192-ad77-e058ca8d41be","year":2022},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.869735Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:e88bf44b8e487ca980cc4f2357a9c055783b929c38b685482b5653008ff1e848","observation_id":"da0b9224-ce70-4796-85c9-5018bb456438","resolution":{"observed_at":"2026-08-10T22:35:18.285830Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:18.262628Z","title":null,"venue":null,"work_id":"228eb7e6-30ee-4faf-b472-b627b008c89e","year":2015},"citing_paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-10T22:35:17.884833Z"},"links":{"citing_paper":"/paper/2501.01278"},"observation_digest":"sha256:bae83841122e746efbdce9998c8e81a87d71dcb83ffeee055d9847b520133784","observation_id":"ab37feea-5983-455e-bfc9-ebf95527d41d","resolution":{"observed_at":"2026-08-10T22:35:18.268858Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01278","last_updated":"2025-01-02T14:21:28Z","latest_version":1,"primary_category":"q-fin.CP","snapshot_observed_at":"2026-08-12T06:08:58.686988Z","submitted_at":"2025-01-02T14:21:28Z","title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks"},"reference_resolution":{"displayed":87,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":55,"verified_exact":7,"verified_fuzzy":25},"total_outbound_references":87},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2501.01278."}