{"as_of":"2026-08-08T23:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a843794839151d57027c3d1acba7e19fe459880f304d94fa90b48c1ed586896","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:42:46.271672Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2509.03852/citation-record","integrity":"/paper/2509.03852/integrity","json":"/paper/2509.03852/citation-record.json","paper":"/paper/2509.03852"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:42:53.961436Z","title":null,"venue":null,"work_id":"94a1cf9a-5cbc-48f7-8852-90846f1261ad","year":2001},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.052207Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:7528de94c48626d589e0fb176651ba3c22009641a69259b95a27d077b835c73f","observation_id":"995ac174-d413-44c4-808f-0bc6eede3d5b","resolution":{"observed_at":"2026-08-05T10:42:54.078990Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:53.656350Z","title":null,"venue":null,"work_id":"27155255-3597-4a52-a945-30458e14df0c","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.057137Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:ab0f0361b5913911eadeb95924ba3a337c300d6754b9be48363ee8af60ffe0a1","observation_id":"cdaee8d6-589b-402c-bf73-dac194eb93f2","resolution":{"observed_at":"2026-08-05T10:42:53.800174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:53.275346Z","title":null,"venue":null,"work_id":"88658eb9-056f-45f0-8763-602275c5fd45","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.061537Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:f0882efb72cfcf5133b0b804c79d7ae90fb70afdd1c4ba36f2362a852499bad6","observation_id":"c70e198e-e4c0-4845-a398-3be3a1c1e8a9","resolution":{"observed_at":"2026-08-05T10:42:53.479847Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:52.904899Z","title":null,"venue":null,"work_id":"62ec5b39-2bea-440b-b816-7c9f9d2ba49c","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.066181Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:1e6ac093055ca8fffa01e8f0655a5752b2e8f64caad177bbf95d76927d322a27","observation_id":"586a43ca-c50b-4057-8b31-b4562e4d86b7","resolution":{"observed_at":"2026-08-05T10:42:53.071034Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:52.609206Z","title":null,"venue":null,"work_id":"06f808d5-564f-4855-8825-4a1b940ba253","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.070453Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:3cc52f8c3f941777f79624986a165808b739db5010cd8d52673cfadafc13b33b","observation_id":"1560443f-3688-4e12-9d80-1eadfc344c5a","resolution":{"observed_at":"2026-08-05T10:42:52.737982Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:52.336946Z","title":null,"venue":null,"work_id":"634394b7-93cb-41f9-accb-753118ee6a0a","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.074833Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:5fbcfdcf6bd95b200a468beb001315abe3de0a59613d304bc8a419215d4293b5","observation_id":"8c2ecece-2c9c-45eb-81a9-4b250db07287","resolution":{"observed_at":"2026-08-05T10:42:52.463662Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:52.075196Z","title":null,"venue":null,"work_id":"a960f7e1-72c1-4340-836b-8253d52e692f","year":2020},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.079484Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:e0d7d2d36c0738619d3ad919c887a5d7e94c6bb82d0bf676559f4c70ab40e99a","observation_id":"33c84e78-6714-4702-b914-d830c11e23e2","resolution":{"observed_at":"2026-08-05T10:42:52.205400Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:51.790811Z","title":null,"venue":null,"work_id":"77052b04-21c8-4383-85b1-21b43871d4d0","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.083566Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:a9642561a6a7888a49b4e6aaf14b8f45a3164bf2ad3e42d59393bbdba4b9d101","observation_id":"9b7910db-0200-4432-937b-85f3724ff083","resolution":{"observed_at":"2026-08-05T10:42:51.940171Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:51.588345Z","title":null,"venue":null,"work_id":"77d37f78-4484-4c88-83a8-05505b34d347","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.087670Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:7ce050b11394f9ceb0bee2ca52c8289e8ffa8e237a13d9df9fc8ba923bc7a147","observation_id":"d2668fa7-1378-4a5e-b658-ebd80eab6fa5","resolution":{"observed_at":"2026-08-05T10:42:51.662922Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:51.454892Z","title":null,"venue":null,"work_id":"39f908ac-3662-4bb5-a768-6ebb9d9287b2","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.091932Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:42d4b96da7eb1bae7921b5cd6a03bb2f0abcc2466b258e7b6807f755422341fd","observation_id":"968e0218-25c5-4952-ad1b-c7afde0eee56","resolution":{"observed_at":"2026-08-05T10:42:51.520049Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:51.256526Z","title":null,"venue":null,"work_id":"0e74e71a-587b-4533-ab62-4438ded06c37","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.095815Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:62f3d122922dc3691a8a99f3691377455a74e79f91007642b99da7a0032834b9","observation_id":"8dab4e67-888b-45e8-bfe2-6c1c26fcc3e1","resolution":{"observed_at":"2026-08-05T10:42:51.356269Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:51.103195Z","title":null,"venue":null,"work_id":"e4270290-0261-4da3-b323-7321f5d2bd35","year":2017},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.099929Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:4e1cca6c5e057082d4488a620f53f0f338dadfa0ff75d483639afc479d019e0f","observation_id":"a73be630-d294-499d-bcf0-5d3b5f6c86f9","resolution":{"observed_at":"2026-08-05T10:42:51.151497Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07135","last_updated":"2025-01-13T08:53:44Z","snapshot_observed_at":"2026-07-06T20:20:09.958277Z","submitted_at":"2025-01-13T08:53:44Z","title":"Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum","version":1},"cited_work":{"arxiv_id":"2501.07135","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.07135","snapshot_observed_at":"2026-08-05T10:42:46.398311Z","title":"Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum","venue":"q-fin.TR","work_id":"482e7c99-dcce-403c-ab3f-3b2bccf8f3b1","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.103915Z"},"links":{"cited_paper":"/paper/2501.07135","citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:bd0aa6226bf066d98adfd3f7b2ea9deda3b2eaefd9f9074546a70a3a45e907e5","observation_id":"b5bc6daf-c019-419b-b038-c206aea6c654","resolution":{"observed_at":"2026-08-05T10:42:46.407866Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:50.922222Z","title":null,"venue":null,"work_id":"0039f539-a959-4f83-b515-984cfac82693","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.108372Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:ffa054e9f94e5e89b0e0bb43131ef3d1de9344869a5fd94c5aa16ceab62e9a0e","observation_id":"0fd07ba3-e1b9-47f2-b6a7-498995edb169","resolution":{"observed_at":"2026-08-05T10:42:50.993879Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11200","last_updated":"2023-08-22T05:23:04Z","snapshot_observed_at":"2026-07-06T16:08:53.004992Z","submitted_at":"2023-08-22T05:23:04Z","title":"SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11200","snapshot_observed_at":"2026-08-05T10:42:46.112458Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.112458Z"},"links":{"cited_paper":"/paper/2308.11200","citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:11dcbc6e2bcbf0dabb88265bab461600c854c99515730402e08b0e9d1889f261","observation_id":"54688422-659d-40b4-b9ac-77f79114e04b","resolution":{"observed_at":"2026-08-05T10:42:46.112458Z","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":"2026-08-05T10:42:50.773469Z","title":null,"venue":null,"work_id":"5d264aa8-9e4f-468f-92fb-f8ca59a43455","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.116738Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:feb48dbea21e686805f7ee05ee6691a0f8a31e8bae13a06a413ae8833e0a7abe","observation_id":"3ec28da9-f7d0-4265-91cf-1cc6228d92ec","resolution":{"observed_at":"2026-08-05T10:42:50.852330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:50.634013Z","title":null,"venue":null,"work_id":"03c5dac4-0d9a-4a06-976f-0cb92c477f74","year":2022},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.120943Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:5283db55658cd110f313ffc949cfd2e7335638ee05bbd086235c73f99c8573a3","observation_id":"c220461d-db5a-4715-9066-308567cc77e0","resolution":{"observed_at":"2026-08-05T10:42:50.710063Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:50.492805Z","title":null,"venue":null,"work_id":"be05e697-55d8-4b8f-97ff-51b01b42289e","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.124910Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:9731b5eae2a350280d75972aa22e2f9cbe155ce25375e8f81bf715553b5f4bbb","observation_id":"8a479359-e455-4b67-a27d-864c7da195cf","resolution":{"observed_at":"2026-08-05T10:42:50.544933Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:50.312751Z","title":null,"venue":null,"work_id":"54a559fb-c4ae-44f3-b886-8e7ca4f9e5e0","year":2022},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.129598Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:509613cbb564326fe3ba3fcfaae403b422ded14d808d62c4e3951203452d5b44","observation_id":"9677332a-eb25-40db-ab4b-b862b790fe2a","resolution":{"observed_at":"2026-08-05T10:42:50.375415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:50.162971Z","title":null,"venue":null,"work_id":"381d30a4-465f-4c7f-9651-f430086fa615","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.134112Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:89a26aff706b58d2f76a33f7a0bdd8b0711462bc49b859fffb739149b63b4849","observation_id":"90410e06-3afd-4f5b-8eec-ed463016a0e6","resolution":{"observed_at":"2026-08-05T10:42:50.220891Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.138379Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.138379Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:23799adf8e1b8b8a23518ef91a7678da0c6e8a92c9c3ddcd49aaa7066e70f30f","observation_id":"b84e0dc3-0a16-41f9-976e-a624408dd87c","resolution":{"observed_at":"2026-08-05T10:42:46.138379Z","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":"2026-08-05T10:42:49.999883Z","title":null,"venue":null,"work_id":"7929e60c-c035-43ad-af2d-e58a2d8030da","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.142447Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:563e3bb1e47a2e37070a90ab62ba4f551cd84c25fbde5a155a2fa7309e6f9f61","observation_id":"906d1f42-cd04-42ec-9477-33e3fb7e19bd","resolution":{"observed_at":"2026-08-05T10:42:50.081214Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:49.862947Z","title":null,"venue":null,"work_id":"d5cccc84-1415-4dc8-8095-dbd4aa3a6676","year":2022},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.146572Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:81a8da8a4d4271af7c9dd20a00c3ed1730f7abb54f30983e7d5a525699a69bba","observation_id":"e12e4a04-e0c6-4dfc-9645-13aa33dd274b","resolution":{"observed_at":"2026-08-05T10:42:49.911982Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:49.723637Z","title":null,"venue":null,"work_id":"49d449d6-d258-48b1-8d96-500b735987d9","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.150550Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:0feb135d01ddfd161901750dd977697c242d4f7a1d3afae3784a9474db7784bf","observation_id":"a267b8b9-362e-434a-bdb0-c3e7ec64e79d","resolution":{"observed_at":"2026-08-05T10:42:49.770620Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:49.556121Z","title":null,"venue":null,"work_id":"bb654476-b8b0-4cb7-b128-6762bdacdb79","year":2020},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.154728Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:eaede6d5a7c523b743a6ef86bec214606da543c9979278bccdb02a52af7f6115","observation_id":"ab393fd6-f6f2-4193-a01a-9adfa01e7eb3","resolution":{"observed_at":"2026-08-05T10:42:49.633153Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09261","last_updated":"2024-12-23T06:08:16Z","snapshot_observed_at":"2026-08-02T17:56:19.687717Z","submitted_at":"2024-01-17T15:12:11Z","title":"MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09261","snapshot_observed_at":"2026-08-05T10:42:46.158655Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.158655Z"},"links":{"cited_paper":"/paper/2401.09261","citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:be788878034e04cc55b487b9ff85ab982caaba322679eccbf59c32d8a40c388e","observation_id":"f026170e-14d6-4d31-90bd-913c079a1dc4","resolution":{"observed_at":"2026-08-05T10:42:46.158655Z","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":"2026-08-05T10:42:49.416561Z","title":null,"venue":null,"work_id":"d79303c7-0831-40dc-aa8f-c5c88523d092","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.162799Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:e49b6ceec5d67b9ffc87e1dd411bc47c62c86e92cdb8a78024ef58fa3ab92273","observation_id":"988a587a-782d-48de-aaf0-da696ebe7ed4","resolution":{"observed_at":"2026-08-05T10:42:49.491836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:49.222898Z","title":null,"venue":null,"work_id":"ee2c5e98-bc5b-467e-a2ce-225935bed9dc","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.167646Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:a53e4e01c21d6da452adeb25d1c7381d4d120790f5c21d37145fc414e8b620c7","observation_id":"5e069f1a-5da1-408b-9826-4c85697d426a","resolution":{"observed_at":"2026-08-05T10:42:49.326738Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:49.051974Z","title":null,"venue":null,"work_id":"93285a35-d3f4-440b-867e-3c09860f73a0","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.171760Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:fb70e3b288547ecaf5c216bdd46d7bd4d24380f73181525658028b5621b4b013","observation_id":"b8edefa6-badd-4337-8d60-f0802e2d5c45","resolution":{"observed_at":"2026-08-05T10:42:49.132032Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.890033Z","title":null,"venue":null,"work_id":"0a9fa0d5-1a99-4e26-8b90-ef5e20f8874f","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.176092Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:c48d4438cd692b9d8969892d57ab4ad3081388f8b9eb748084dddae33349d690","observation_id":"52100d8c-a37f-4393-be3a-f20818f5cfda","resolution":{"observed_at":"2026-08-05T10:42:48.986761Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.759382Z","title":null,"venue":null,"work_id":"22ef311e-7045-4b31-aed0-abd0996347af","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.180168Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:e5ee69da8411b8910340dfb877f5eb93ccc264ecee184cc9447cb3de76a10f2e","observation_id":"104882e5-8ae3-4eeb-a8cf-a085fca00e99","resolution":{"observed_at":"2026-08-05T10:42:48.819830Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.631349Z","title":null,"venue":null,"work_id":"6c2065e8-536d-4836-97c1-c676d735d507","year":1930},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.184258Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:14d9d78d5632548c7b12c059ad9729e4a44a1748d029d629be38ac7b28a32901","observation_id":"5c55fa23-f777-4453-a425-07482062d3b8","resolution":{"observed_at":"2026-08-05T10:42:48.688565Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.501971Z","title":null,"venue":null,"work_id":"ea84da7b-d0ca-4ed7-bf21-b49783003793","year":2003},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.188566Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:2a983c0e45c67a3890a11a381c20a6527ec612b407af4616b9d74a098429411b","observation_id":"762eb825-0d20-4ed6-ad9a-168301b3c98c","resolution":{"observed_at":"2026-08-05T10:42:48.562473Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.00518","last_updated":"2023-07-02T08:53:10Z","snapshot_observed_at":"2026-07-06T15:49:19.631265Z","submitted_at":"2023-07-02T08:53:10Z","title":"DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting","version":1},"cited_work":{"arxiv_id":"2307.00518","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.00518","snapshot_observed_at":"2026-08-05T10:42:46.343184Z","title":"DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting","venue":"cs.LG","work_id":"259281c5-94db-4e4e-b137-8f0e8d5616cd","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.192764Z"},"links":{"cited_paper":"/paper/2307.00518","citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:bad95f0fc4d74faa659924116ee25a6c9d20cb5e50912b44eae9ddcab479b116","observation_id":"8865dbed-9802-49f7-8b04-6d09ddd31cd7","resolution":{"observed_at":"2026-08-05T10:42:46.348091Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.330147Z","title":null,"venue":null,"work_id":"0cf6e3a5-6490-41ed-98b8-92639916ab89","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.197291Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:091cc4596b6fd6e1310e266803908f2ef05a6f1bb63cf12314111ece071ca201","observation_id":"6851c644-1bbe-4a35-8749-174d35d47cb2","resolution":{"observed_at":"2026-08-05T10:42:48.410959Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02217","last_updated":"2025-09-02T11:37:08Z","snapshot_observed_at":"2026-08-05T11:50:06.128357Z","submitted_at":"2025-09-02T11:37:08Z","title":"ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2509.02217","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.02217","snapshot_observed_at":"2026-08-05T10:42:46.316518Z","title":"ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting","venue":"cs.LG","work_id":"41f9898a-e474-4dd4-b0a2-ab84aaf107b2","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.201813Z"},"links":{"cited_paper":"/paper/2509.02217","citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:486b8a07d1c9ea0ff9cb72e67c6bf35ea8e3c57cc46c7cefbbae51d340dde11d","observation_id":"dbb20031-bc8d-423b-b269-dc3b661c7d91","resolution":{"observed_at":"2026-08-05T10:42:46.324887Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.161397Z","title":null,"venue":null,"work_id":"9ebf3057-acbe-4c05-801f-3602b32ed1c5","year":2004},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.206226Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:c1b47caf27cb2aa44a71f3e2cdd85a9c77f4f2203feb192de10d15e8a2c56f36","observation_id":"3330c7c1-053f-45f5-9d52-3948797e8468","resolution":{"observed_at":"2026-08-05T10:42:48.232255Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:48.018975Z","title":null,"venue":null,"work_id":"8341d406-b527-496f-96eb-0372a18d596d","year":2025},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.210403Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:6fd6abcfe7f084e13683a049d0afb23f5fef3fa71c51dabf7c5f2cd067f2a58c","observation_id":"029719e0-9ce1-40b6-92aa-c9c97317fc31","resolution":{"observed_at":"2026-08-05T10:42:48.072821Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:47.815689Z","title":null,"venue":null,"work_id":"5776f82c-aa79-49e0-9d4d-d188636e71bb","year":null},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.214695Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:8ede04e53119baca3e90056953a4cafb47a31e79de0cb2473a009dc5e34123c7","observation_id":"86ef194a-e46f-4b2a-8958-6e0eca674f66","resolution":{"observed_at":"2026-08-05T10:42:47.918667Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:47.396388Z","title":null,"venue":null,"work_id":"83572518-edf5-43c3-ade2-15d75f2cefc9","year":2021},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.223324Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:2c4b9489f33c0906498ddbba52b4b6f2e06e31a209e85b4ee76662de0df6a34e","observation_id":"3b24b332-3ab2-47fd-aa92-87cbf4de9b24","resolution":{"observed_at":"2026-08-05T10:42:47.499265Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:47.221321Z","title":null,"venue":null,"work_id":"28ba56f4-e6f6-4f61-bf13-80e31c3c2c9f","year":2020},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.227466Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:7d0670a683bc49ea464d854eb6317e37bb5215d29cfd7ecf370301d74af012b6","observation_id":"e0c58b43-06a9-4a06-a07c-9c8732e9a301","resolution":{"observed_at":"2026-08-05T10:42:47.320927Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:47.036303Z","title":null,"venue":null,"work_id":"faf8b1fe-1e7a-4e81-867f-ab087596e328","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.231485Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:6e42e240da9e0f0caffa8f36c2c6c86f2da81393d93682a808f8fbca01203a33","observation_id":"4d08768f-9068-4074-8754-4c950d48a5f2","resolution":{"observed_at":"2026-08-05T10:42:47.118122Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.235658Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.235658Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:667f0a95716301e951e970036b4775dc63357e35dcaa3044bedbc2198f508ee5","observation_id":"714d3629-5ef7-48c3-a311-824914f6b90c","resolution":{"observed_at":"2026-08-05T10:42:46.235658Z","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":"2026-08-05T10:42:46.869714Z","title":null,"venue":null,"work_id":"a2a9eef3-bd3b-4506-9ab6-3e76fbcccea6","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.239694Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:6cea3f3e63574a6b445843cbdebdbd8e4e672269d7779b74a5e889858d989fc9","observation_id":"db3dead7-66cd-4f65-b903-88d1b4991831","resolution":{"observed_at":"2026-08-05T10:42:46.953276Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.724579Z","title":null,"venue":null,"work_id":"7a2b1d3c-56ca-49ac-b6e8-46fd38a912fe","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.243870Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:47f512a6cd5024e5439f35851bb70165e48f2d0f0bd750079ea4c441ee365bc7","observation_id":"c9be2671-95b7-466a-9c78-d9f30411b375","resolution":{"observed_at":"2026-08-05T10:42:46.782839Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.573367Z","title":null,"venue":null,"work_id":"a10b0d27-87ea-4535-8f95-b8538aa7976d","year":2023},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.247778Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:1d926310d46c8f08da85bc9723215c37f0970ad79ad375e4577396989c2555fd","observation_id":"ed106e47-ad92-4363-848a-f7d238b1e130","resolution":{"observed_at":"2026-08-05T10:42:46.618063Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.543417Z","title":null,"venue":null,"work_id":"9787dcec-f083-4d49-ac6d-7f2236a276fb","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.252098Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:978b1d69ea8012f6a9df15cc9644756f1c9384491208501f684a10648cc930a5","observation_id":"56afcc5d-1845-40d2-a79d-2bf05c872d53","resolution":{"observed_at":"2026-08-05T10:42:46.554866Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.510912Z","title":null,"venue":null,"work_id":"ef260ff8-6d65-4977-ae77-e0ca6a483d46","year":2021},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.255936Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:4c3d09838e69cde953ed1eceb1861469b7bef4ce079e6c04b6ac4eae00f75824","observation_id":"ddc78d9b-bc4e-43fa-9681-3ed073de0186","resolution":{"observed_at":"2026-08-05T10:42:46.523408Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.477633Z","title":null,"venue":null,"work_id":"46985920-378a-4a37-9238-4ec1df4f0489","year":2022},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.261113Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:e694b7f016209e8a4f328037cce7818ae7d0e76c79c6b673c30bf07609623313","observation_id":"fc1eff42-71f4-4fce-a121-b6ceac0e58a0","resolution":{"observed_at":"2026-08-05T10:42:46.490780Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.450013Z","title":null,"venue":null,"work_id":"b9e7f0cb-f267-4829-b9c5-78a57374afdc","year":null},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.266316Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:7e8989628a6f526ff2ba518412000898a1c2b0c4d39382bc25064d0d725a2105","observation_id":"b97ea6c7-c73d-4392-8ad0-4fe141d83b7c","resolution":{"observed_at":"2026-08-05T10:42:46.459833Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:47.579778Z","title":"In International Conference on Learning Representations","venue":null,"work_id":"673142dc-b10b-43b0-a360-abbdd9b17a08","year":null},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.219075Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:81daea66b91e3643e4b83e8c1185916e436fd2b776b9af8f2f2be7be179b3493","observation_id":"7b5b4ea3-6936-4d1c-9f4f-84f79ddb47b9","resolution":{"observed_at":"2026-08-05T10:42:47.681700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:42:46.422933Z","title":"Applied Energy 364 (2024), 123194","venue":null,"work_id":"7f0bae07-94be-40cb-a3c6-eae5495f0b0b","year":2024},"citing_paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T10:42:46.271672Z"},"links":{"citing_paper":"/paper/2509.03852"},"observation_digest":"sha256:5b328197bac2d1f2696b527efb310cd09836db0cda26c8d451bb0e48cf188e07","observation_id":"af846fbe-830b-473c-95ab-9fe069d6de5c","resolution":{"observed_at":"2026-08-05T10:42:46.432720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03852","last_updated":"2025-09-04T03:28:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T10:42:44.340458Z","submitted_at":"2025-09-04T03:28:42Z","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":47,"verified_exact":3,"verified_fuzzy":2},"total_outbound_references":52},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2509.03852."}