{"as_of":"2026-08-22T10:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c0243f59e9fdebe0e413c4c53e5607c18e46af5c38df0bbf804ae49246981a16","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T05:52:19.974627Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2509.04921/citation-record","integrity":"/paper/2509.04921/integrity","json":"/paper/2509.04921/citation-record.json","paper":"/paper/2509.04921"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:52:20.254815Z","title":"Maddix, Michael W","venue":null,"work_id":"1719f277-5ae9-4cf3-a3fe-938957c48699","year":2024},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.926332Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:7fbb46347e814e5ffa13dda53931c31fa8886f847d9d06936477cf054b110661","observation_id":"cdbdbe6a-69d2-4280-976d-615c7aa77f40","resolution":{"observed_at":"2026-08-05T05:52:20.257776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09385","last_updated":"2024-10-12T06:35:18Z","snapshot_observed_at":"2026-08-21T14:04:14.980166Z","submitted_at":"2024-10-12T06:35:18Z","title":"Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09385","snapshot_observed_at":"2026-08-05T05:52:19.929834Z","title":"Mamba4Cast: Efficient Zero-Shot Time Series Forecas ting with State Space Models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.929834Z"},"links":{"cited_paper":"/paper/2410.09385","citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:041a6c93eed051c9db434ef3993191674636f60e4b1f0c36e9d3e6230706f1d5","observation_id":"b2a459c1-0ccf-42c8-9270-efe9d973f70a","resolution":{"observed_at":"2026-08-05T05:52:19.929834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18834","last_updated":"2026-06-01T07:37:08Z","snapshot_observed_at":"2026-08-16T12:55:08.646938Z","submitted_at":"2025-02-26T05:19:16Z","title":"FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18834","snapshot_observed_at":"2026-08-05T05:52:19.933216Z","title":"FinTSB: A Comprehensive and Prac tical Benchmark for Financial Time Series Forecasting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.933216Z"},"links":{"cited_paper":"/paper/2502.18834","citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:28469fc950d5f5d86865f0845b746f880eed3076ae17b581cf7ec3dfd820d3c6","observation_id":"62e9a347-74e5-4e75-9a94-4eeedb28d73d","resolution":{"observed_at":"2026-08-05T05:52:19.933216Z","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":"2026-08-05T05:52:19.936774Z","title":"Panda: A Pre trained Forecast Model for Universal Representation of Chaotic Dynamics","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.936774Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:0679220d06e99e50c51395bba1bbadcfcb4b75845a7ca9a6fd62c6c28e533b80","observation_id":"d2330a32-1696-4930-b9a6-712d38c4b176","resolution":{"observed_at":"2026-08-05T05:52:19.936774Z","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":"2026-08-05T05:52:19.939927Z","title":"True Zer o-Shot Inference of Dynamical Systems Preserving Long-Term Statistics","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.939927Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:34249b427b1fdd8886080ab39ee4589eb1ad92c1b034aba1363b2f995e92cb37","observation_id":"68cc4d8e-9b30-4170-ab8e-d9012017d496","resolution":{"observed_at":"2026-08-05T05:52:19.939927Z","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":"2026-08-05T05:52:20.247220Z","title":"McKenzie","venue":null,"work_id":"d8be002c-26df-4b25-8df5-ab277873121a","year":null},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.943351Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:e2201a07a1a06c240d43c1fc8707878414eaa66ad8efce05e782621c9f50dae1","observation_id":"4d919012-9a7e-42cb-8818-2944b3f04729","resolution":{"observed_at":"2026-08-05T05:52:20.249924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.238958Z","title":null,"venue":null,"work_id":"ef067d88-23ff-425d-ba51-a8e8f7c522af","year":1991},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.946289Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:b72353b4c42003a4cffb881584d62bfd878596018ed15e12a0f0832192142433","observation_id":"a9e585c0-9386-4ed3-a9a5-42c9acc68aba","resolution":{"observed_at":"2026-08-05T05:52:20.241914Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.230400Z","title":"Scheinkman and Blake LeBaron","venue":null,"work_id":"fb31a4c0-5579-49aa-8efd-ac32e1ce65f2","year":1989},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.949412Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:add87b21fe40cbd213e479f830ec4e1caf75195288b2aeb999e2418f887cd93e","observation_id":"ab40337c-05d4-48f6-bd19-1b819103c858","resolution":{"observed_at":"2026-08-05T05:52:20.233550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.221511Z","title":"Sandubete and Lorenzo Escot","venue":null,"work_id":"39c5db47-2a08-4b4c-908e-c95833adef88","year":2020},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.952236Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:b566a199fe3d3165286a59212941c118e1b5412e690716b33455e11d57f537e5","observation_id":"e5af827f-6b30-4edd-a39f-5c0228c3fc3e","resolution":{"observed_at":"2026-08-05T05:52:20.224660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.213160Z","title":"Chaotic Analysis of the Foreign Exchange Rates","venue":null,"work_id":"2c7fdc61-09bb-4056-a0b7-d44a4598d164","year":2006},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.955108Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:148312d85b876dc993e11241ed9bce522c4cc786c81b4f8d3c45674ad407d459","observation_id":"ef4628d5-d172-42be-b955-0b50456dea1e","resolution":{"observed_at":"2026-08-05T05:52:20.216284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.204609Z","title":"Expectations, Price Fluctuations and L orenz Attractor","venue":null,"work_id":"3f981cc5-6941-475c-9b60-624a9ebd854b","year":2018},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.957945Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:ccb66cf0e7f813ed3d3ef3d5b2711d0a0f89d603230436ff4c02bbbe533e8cb1","observation_id":"3f48eeae-9864-4632-a62b-882547399190","resolution":{"observed_at":"2026-08-05T05:52:20.207763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.195014Z","title":"Financial Time Series An alysis Using Total-CApEn and A vg-CApEn with Cumulative Histogram Matrix","venue":null,"work_id":"fcd19561-7b74-48df-a528-c1e53f7e4083","year":2018},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.960627Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:1dad88e49cf70c976921b7c7059b661b486eee5b5c00581ed196043615c2b5f1","observation_id":"e7b021d4-48f8-464a-832f-1aa134aa5044","resolution":{"observed_at":"2026-08-05T05:52:20.198598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.186371Z","title":"Seasonal Mackey –Glass–GARCH Process and Short-Term Dynamics","venue":null,"work_id":"551aecde-9ce5-4028-8a4c-0b2c96adf5db","year":2010},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.963672Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:f3d08c0dd05ec4a47c67c75ebc11f61edf82a7cbe385f5e9695f953ae839deb0","observation_id":"12088695-6c32-4d9e-8924-8cdd6eaf1f17","resolution":{"observed_at":"2026-08-05T05:52:20.189307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.177550Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":"e52d585c-8c06-45e5-90f8-7c0c61954657","year":2017},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.966422Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:6ea05cce6e6e03e413da6043e86aa53092ad954445352eb83cdce37142f53176","observation_id":"f31c89ea-2ee9-44fc-9de8-51b0cb5b6f79","resolution":{"observed_at":"2026-08-05T05:52:20.180659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.167915Z","title":"I nformer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","venue":null,"work_id":"13325d75-a0ad-4710-a347-cbf267a0061e","year":2021},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.969114Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:8e155b042c756ee217b72af3ffa481a5d7c56389a717901053bc99b8e5c54a4d","observation_id":"8dc9f1ec-e152-4ae1-ae37-51481bb99c84","resolution":{"observed_at":"2026-08-05T05:52:20.171635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.159525Z","title":null,"venue":null,"work_id":"2bd1f7b2-3d2c-495e-a82f-08bd455f9ce8","year":2022},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.971935Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:3cb8c6d94eb2772cc15c25399007cc253d8f74646a190c90d2a59dbddbd08793","observation_id":"af8227bc-6472-4804-9477-eda379a35bd5","resolution":{"observed_at":"2026-08-05T05:52:20.162397Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-05T05:52:20.149520Z","title":null,"venue":null,"work_id":"a9383594-bbd8-4a65-a779-b445db19b8fd","year":2020},"citing_paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T05:52:19.974627Z"},"links":{"citing_paper":"/paper/2509.04921"},"observation_digest":"sha256:eb31d0b722420b8769cb4c83b2ce63e95e4620c09dc83c35719da96a98e2b9c2","observation_id":"2cfc962e-53b6-4b01-a78e-a188cb4b97ae","resolution":{"observed_at":"2026-08-05T05:52:20.153636Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.04921","last_updated":"2025-09-05T08:40:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T11:37:35.281990Z","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":17},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2509.04921."}