{"as_of":"2026-08-14T19:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5e80327f910447b9c9c137624dd4a68393fdf50989b14e86a4e1cc519d88bf7e","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:40:23.245732Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.12953/citation-record","integrity":"/paper/2506.12953/integrity","json":"/paper/2506.12953/citation-record.json","paper":"/paper/2506.12953"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.579526Z","title":null,"venue":null,"work_id":"1761859e-62fe-42bd-adab-0825fd5b43e9","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.627168Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:aa44906fb5e3aaa756749891f8a3747d910da1daff81738f816c543e5c7a5f34","observation_id":"2787d5d0-bd98-46c5-9336-2c1c675088c6","resolution":{"observed_at":"2026-08-07T00:40:24.664172Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.437703Z","title":null,"venue":null,"work_id":"57967e50-9fc4-4603-b768-86ea0328dc2f","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.716902Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:e0169f6cc0fd9f91b1aa6cb1245c1730490dc3dfe260e19f8072c0062643c120","observation_id":"11c98e20-77a7-4d94-81c4-2a8b0b7ffd66","resolution":{"observed_at":"2026-08-07T00:40:24.473282Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.311374Z","title":null,"venue":null,"work_id":"a6f69992-6f8b-48e9-8d10-1db070b8a2bd","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.783062Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:768fc9eebadc4371687256ca525b12c094b75a91e193515bd8d41215c3d1ea63","observation_id":"da262540-b4ee-40ae-a5c2-2460125d8d1f","resolution":{"observed_at":"2026-08-07T00:40:24.356978Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.182598Z","title":"series\": 96 raw numbers (Humidity, 10-min cadence) -","venue":null,"work_id":"ffaa3cda-8eeb-41d9-9a76-46be30ffb350","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.862752Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:be5af411ec026ea4c9f92505bd1ae6f1c7e869d50346fb48b06bf4b898e01c00","observation_id":"58639b5d-0519-49d6-a1e9-f692f950773b","resolution":{"observed_at":"2026-08-07T00:40:24.237000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.053721Z","title":null,"venue":null,"work_id":"dc80cc0f-405e-4612-a316-2d55d27a7376","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.919511Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:07a7b3b373b0cfb1db36257ab5c02d50f80c8053de6baac6963d76c5c184abe1","observation_id":"81ac0446-e0da-4043-9ec0-e8499e11e6f1","resolution":{"observed_at":"2026-08-07T00:40:24.113994Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:23.919748Z","title":null,"venue":null,"work_id":"9f1c3713-53ff-4bc6-9d1d-bfd1c5fcde98","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.986943Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:699cf3034a34806c1848493db57ebfb54a9d554c6d117ed91ee5b7f5995ee03f","observation_id":"a1b41283-e0a5-4b74-935c-0d35ab191978","resolution":{"observed_at":"2026-08-07T00:40:23.984391Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:23.785235Z","title":"series\": 96 raw numbers (Humidity, 10-min cadence) -","venue":null,"work_id":"d306d698-c4fc-4ea6-908f-d35d1ba0441b","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:23.049092Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:2365e284365de92014f00e07014400bce73191fae074b3e128ef5cfc5692e47c","observation_id":"51dfd919-ca88-404d-855b-537a652fa105","resolution":{"observed_at":"2026-08-07T00:40:23.833206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:23.632013Z","title":null,"venue":null,"work_id":"c051697d-8995-4dcf-b6cc-5059a0744c33","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:23.115574Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:b9a08568360a1f362d6b987355d88b16c88bee9b9f4ed0814e30ac4a26965e14","observation_id":"cd908f13-2ca8-4680-8edd-ed922eebfb28","resolution":{"observed_at":"2026-08-07T00:40:23.693060Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:23.488303Z","title":null,"venue":null,"work_id":"ac8a1277-b09d-41f6-bc65-9c734ae03551","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:23.174531Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:9801f76e50b6c54aae1d95042780095f9783ee576b78d21e9343ad0db8c5bb90","observation_id":"9e05cd76-5026-4a70-afce-5147fac69a23","resolution":{"observed_at":"2026-08-07T00:40:23.565303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:23.350337Z","title":"Output format: [(v1;slot1), (v2;slot2), (v3;slot3)] [(v2;slot2), (v3;slot3), (v4;slot4)]","venue":null,"work_id":"3c1018a1-4240-409f-9f0b-33a24676703a","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:23.245732Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:541562e5bced5bb24367c60f6c28763ba80a3b397dcaaa9cd82539828682976d","observation_id":"1a74c940-ec9f-491a-b828-e092f6a8390f","resolution":{"observed_at":"2026-08-07T00:40:23.413135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:24.745279Z","title":"Xinyu Zhou, Zhengyuan Ding, Shuo Ren, Yutao Chen, Xinhui Huang, Jianhao Shi, and Wayne Xin Zhao","venue":null,"work_id":"df0dba09-9486-4178-b0f7-3ba915c8ce86","year":null},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.468250Z"},"links":{"citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:67d555732fe280967f0aed4fbda146d332dc5cc9f052a144e047e5aa95e2a1af","observation_id":"bd286de4-3b74-4ce1-9b7a-369b8ed66ad1","resolution":{"observed_at":"2026-08-07T00:40:24.852872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05793","last_updated":"2025-05-01T05:05:29Z","snapshot_observed_at":"2026-08-12T22:16:08.667254Z","submitted_at":"2024-10-24T07:43:55Z","title":"A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05793","snapshot_observed_at":"2026-08-07T00:40:22.562353Z","title":"recency-first","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:22.562353Z"},"links":{"cited_paper":"/paper/2411.05793","citing_paper":"/paper/2506.12953"},"observation_digest":"sha256:81e1ac04a3869c245f75768888f753e17f584387fc69c132545ae8808f5dad7b","observation_id":"2ed91382-a1f3-4a85-ac0b-9155e1da8bd5","resolution":{"observed_at":"2026-08-07T00:40:22.562353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.12953","last_updated":"2025-06-15T19:42:58Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T10:23:14.797816Z","submitted_at":"2025-06-15T19:42:58Z","title":"Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2506.12953."}