{"as_of":"2026-08-18T18:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6c83629f48ac4e8d735171e95ecbbc998f1d62efe5547f9a6e4dc50bd04c5ea0","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:09:51.381060Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T23:17:29.022593Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-11T23:24:18.673005Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02525","last_updated":"2024-12-03T16:18:42Z","snapshot_observed_at":"2026-08-18T14:31:57.791984Z","submitted_at":"2024-12-03T16:18:42Z","title":"LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.673005Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:cb35cd856f914e3a3dd61d38d0aab405551d4ea85032b0ec0c3934a4dbb8c93a","observation_id":"a0858949-9909-48c2-af05-ee082b63ee3a","resolution":{"observed_at":"2026-08-11T23:24:18.673005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-10T23:21:23.764856Z","title":"Temporal data meets llm--explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.20810","last_updated":"2024-12-30T09:06:47Z","snapshot_observed_at":"2026-08-15T07:52:59.758574Z","submitted_at":"2024-12-30T09:06:47Z","title":"TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T23:21:23.764856Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2412.20810"},"observation_digest":"sha256:ba838b21e16ec286b28d562b30f213eaa95133552f7581935f81d98c47e592ea","observation_id":"aaad8135-a316-4c92-96ed-22a176f916be","resolution":{"observed_at":"2026-08-10T23:21:23.764856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-08T18:22:22.868872Z","title":"Temporal Data Meets LLM -Explainable Financial Time Series Forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05699","last_updated":"2025-02-08T21:39:07Z","snapshot_observed_at":"2026-08-18T14:31:52.126897Z","submitted_at":"2025-02-08T21:39:07Z","title":"Context information can be more important than reasoning for time series forecasting with a large language model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T18:22:22.868872Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2502.05699"},"observation_digest":"sha256:8480e6cbaadea194fb894b8e060961be5e180d0a71894537b5489b53622e3d8f","observation_id":"a4486d49-5122-4dc8-ba61-d06248bd6e70","resolution":{"observed_at":"2026-08-08T18:22:22.868872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-08T18:22:10.311028Z","title":"Temporal Data Meets LLM -Explainable Financial Time Series Forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05701","last_updated":"2025-02-08T21:42:14Z","snapshot_observed_at":"2026-08-17T21:56:21.879076Z","submitted_at":"2025-02-08T21:42:14Z","title":"TOKON: TOKenization-Optimized Normalization for time series analysis with a large language model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T18:22:10.311028Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2502.05701"},"observation_digest":"sha256:59de6d7f0dee5219010143606825503e8a9f2d028f478eeec37e3cd7486067e7","observation_id":"ff64d697-e009-4290-b796-ec8bad04655c","resolution":{"observed_at":"2026-08-08T18:22:10.311028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-08T17:40:15.274766Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05878","last_updated":"2025-06-07T00:43:58Z","snapshot_observed_at":"2026-08-14T20:18:20.115012Z","submitted_at":"2025-02-09T12:26:05Z","title":"Retrieval-augmented Large Language Models for Financial Time Series Forecasting","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T17:40:15.274766Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2502.05878"},"observation_digest":"sha256:d7c40a0ca1a401f9d238eab55516305747b0333cd5d306009bb284d86bb217b9","observation_id":"c2337bad-8657-421b-bd00-ef7aba3d5efd","resolution":{"observed_at":"2026-08-08T17:40:15.274766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-16T11:09:51.381060Z","title":"Temporal data meets LLM–explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.16432","last_updated":"2025-08-08T11:14:54Z","snapshot_observed_at":"2026-08-18T09:14:43.856337Z","submitted_at":"2025-04-23T05:34:49Z","title":"iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:09:51.381060Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2504.16432"},"observation_digest":"sha256:dc04958e39028491d6fb0fd511870eb0a163b6ef3c5c9f8e7420d0f03d7aa757","observation_id":"42c84b74-d846-47e9-a121-74c74b4dd05f","resolution":{"observed_at":"2026-08-16T11:09:51.381060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-16T05:51:10.592570Z","title":"Temporal Data Meets LLM–Explainable Financial Time Series Forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19669","last_updated":"2025-04-28T10:56:23Z","snapshot_observed_at":"2026-08-17T04:58:11.686909Z","submitted_at":"2025-04-28T10:56:23Z","title":"Multimodal Conditioned Diffusive Time Series Forecasting","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-16T05:51:10.592570Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2504.19669"},"observation_digest":"sha256:724906a6c7764e107fdea35736e84e2207c37a9810589fadf521d81256d4c1db","observation_id":"f6864dd3-53ee-44c4-852a-112c7cc4b3f3","resolution":{"observed_at":"2026-08-16T05:51:10.592570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-16T00:57:28.926133Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.02417","last_updated":"2025-05-08T08:30:12Z","snapshot_observed_at":"2026-08-16T23:47:20.966592Z","submitted_at":"2025-05-05T07:22:54Z","title":"T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T00:57:28.926133Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2505.02417"},"observation_digest":"sha256:28d1b5a303621ab1c906d58e24a065a75028bfe7b52096e075e55a91b7043f3a","observation_id":"c811979e-ca65-420d-97a3-22880d6c7485","resolution":{"observed_at":"2026-08-16T00:57:28.926133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-15T21:26:50.938235Z","title":"Temporal data meets llm--explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09852","last_updated":"2025-05-14T23:24:22Z","snapshot_observed_at":"2026-08-17T18:14:49.629328Z","submitted_at":"2025-05-14T23:24:22Z","title":"Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T21:26:50.938235Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2505.09852"},"observation_digest":"sha256:f9f8d60bc13601618f32ee1c9aab9ca1f1e84cf657c1ee2bf6d5126ce28cc74f","observation_id":"f2d4ecc9-4627-47c9-b343-351fcacee672","resolution":{"observed_at":"2026-08-15T21:26:50.938235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T12:06:59.740927Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00531","last_updated":"2025-05-31T12:27:17Z","snapshot_observed_at":"2026-08-16T14:43:26.149047Z","submitted_at":"2025-05-31T12:27:17Z","title":"M2WLLM: Multi-Modal Multi-Task Ultra-Short-term Wind Power Prediction Algorithm Based on Large Language Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:06:59.740927Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.00531"},"observation_digest":"sha256:ff396c6bc40fa385e30ce326aa5de7769bcd3fecaafc4722f9726253f74a6a34","observation_id":"f4ea6e4a-1b8c-4fdd-9516-146f7ec73849","resolution":{"observed_at":"2026-08-07T12:06:59.740927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T11:49:48.365942Z","title":"Temporal data meets llm–explainable financial time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.01364","last_updated":"2025-06-02T06:46:42Z","snapshot_observed_at":"2026-08-16T15:17:16.341541Z","submitted_at":"2025-06-02T06:46:42Z","title":"Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review","version":1},"reference_index":199,"source":"pdf_text","source_observed_at":"2026-08-07T11:49:48.365942Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.01364"},"observation_digest":"sha256:3b9c345f0836446d395cc34ab937e501f52cfdac4513fbc527a25275063ff639","observation_id":"4f4a0c38-fe0c-403d-9268-d32617cf9e9a","resolution":{"observed_at":"2026-08-07T11:49:48.365942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T11:33:34.324219Z","title":"Temporal data meets llm–explainable financial time series forecasting.arXiv preprint arXiv:2306.11025, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02126","last_updated":"2025-06-02T18:01:00Z","snapshot_observed_at":"2026-08-16T15:42:44.759793Z","submitted_at":"2025-06-02T18:01:00Z","title":"Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:33:34.324219Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.02126"},"observation_digest":"sha256:dfc61a695b0f0fa94e38d05bf018df081d91251c3b0f401ab7dc0d85cd3d144f","observation_id":"2334dc10-f27f-4747-8d66-2b915d69ccee","resolution":{"observed_at":"2026-08-07T11:33:34.324219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T10:41:38.993706Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.04699","last_updated":"2025-06-05T07:21:13Z","snapshot_observed_at":"2026-08-14T01:39:10.774506Z","submitted_at":"2025-06-05T07:21:13Z","title":"Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T10:41:38.993706Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.04699"},"observation_digest":"sha256:e46555dc2c3165e2bcb46185a4f30c03cd1961edeac12ac2dca302888675a9fe","observation_id":"92643566-5096-44da-b5ed-f356ec7f88ca","resolution":{"observed_at":"2026-08-07T10:41:38.993706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-15T22:15:53.497148Z","title":"Temporal data meets llm--explainable financial time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06288","last_updated":"2025-05-12T16:53:29Z","snapshot_observed_at":"2026-08-18T14:40:33.041128Z","submitted_at":"2025-05-12T16:53:29Z","title":"DELPHYNE: A Pre-Trained Model for General and Financial Time Series","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T22:15:53.497148Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.06288"},"observation_digest":"sha256:421cb0ea5150d868df0a4c3c7f2f4a65a53beecda0a77a302db894ce9566a5ca","observation_id":"0c877836-0d29-4c60-8506-41c525926e5d","resolution":{"observed_at":"2026-08-15T22:15:53.497148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T15:26:53.530368Z","title":"Temporal data meets llm–explainable financial time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11040","last_updated":"2025-05-21T04:45:11Z","snapshot_observed_at":"2026-08-12T21:56:26.013964Z","submitted_at":"2025-05-21T04:45:11Z","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:26:53.530368Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.11040"},"observation_digest":"sha256:5104925b00417a7f7d590fc485e3465614bff323c9a5e22e60c73603f19f0c41","observation_id":"60ce54a7-d675-4b5f-ae57-021dfe903e64","resolution":{"observed_at":"2026-08-07T15:26:53.530368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T01:02:18.416805Z","title":"arXiv preprint arXiv:2306.11025 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12240","last_updated":"2025-06-13T21:41:07Z","snapshot_observed_at":"2026-08-13T10:29:12.184508Z","submitted_at":"2025-06-13T21:41:07Z","title":"Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T01:02:18.416805Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.12240"},"observation_digest":"sha256:758932ebb7064b843021e77f6e53ee872f6f8051d85011c64f7c8fd6527918af","observation_id":"b4ad5ea3-e5b8-49ed-a88f-84779c2f317e","resolution":{"observed_at":"2026-08-07T01:02:18.416805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-07T00:42:52.512730Z","title":"arXiv preprint arXiv:2306.11025","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.13036","last_updated":"2025-06-16T02:02:30Z","snapshot_observed_at":"2026-08-15T07:38:23.217337Z","submitted_at":"2025-06-16T02:02:30Z","title":"Forecast-Then-Optimize Deep Learning Methods","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T00:42:52.512730Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2506.13036"},"observation_digest":"sha256:32ab40ef083d2aaccd652dfb11a447ec3d22cc9977384d9d2f2b5e2216a63cec","observation_id":"1305ccc2-b9c0-4261-aac9-4563522501c4","resolution":{"observed_at":"2026-08-07T00:42:52.512730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-06T21:15:10.164820Z","title":"Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.00718","last_updated":"2025-07-01T12:57:18Z","snapshot_observed_at":"2026-08-16T10:25:36.618417Z","submitted_at":"2025-07-01T12:57:18Z","title":"AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:15:10.164820Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2507.00718"},"observation_digest":"sha256:6e5e8395fe46d3f5a74c289ba226246fa36463005fab0a39d620aa62c96d8c66","observation_id":"c92b1f75-0204-41f2-8b25-38adcf944d88","resolution":{"observed_at":"2026-08-06T21:15:10.164820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-06T18:49:20.906985Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07296","last_updated":"2025-07-09T21:43:06Z","snapshot_observed_at":"2026-08-18T00:06:49.684722Z","submitted_at":"2025-07-09T21:43:06Z","title":"Time Series Foundation Models for Multivariate Financial Time Series Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:49:20.906985Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2507.07296"},"observation_digest":"sha256:8f6afe33112599ecfa229b20f4ed55d15eae3c265e1ac5d001bcfbaac697a957","observation_id":"4a866a1a-2ebb-4b71-b40f-8f0fc3445046","resolution":{"observed_at":"2026-08-06T18:49:20.906985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-06T15:04:11.202208Z","title":"Temporal data meets llm–explainable financial time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17016","last_updated":"2025-07-22T21:03:13Z","snapshot_observed_at":"2026-08-15T03:36:42.412952Z","submitted_at":"2025-07-22T21:03:13Z","title":"Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:04:11.202208Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2507.17016"},"observation_digest":"sha256:10c87477fb6f3ab542821eda8d3b13f3ba97e31d9002e072900ac868a398c882","observation_id":"c6f8e752-9573-4f07-a8f9-b7a11554db65","resolution":{"observed_at":"2026-08-06T15:04:11.202208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-05T15:10:28.525513Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20437","last_updated":"2025-08-28T05:27:45Z","snapshot_observed_at":"2026-08-14T09:35:08.471176Z","submitted_at":"2025-08-28T05:27:45Z","title":"On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-05T15:10:28.525513Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2508.20437"},"observation_digest":"sha256:a68aa112c273432545eb34b5c2cf3e18be0dca1737579f8c69f87b167b875b33","observation_id":"6d23b671-12cb-41d4-a809-c2823b185b3f","resolution":{"observed_at":"2026-08-05T15:10:28.525513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2509.05215","last_updated":"2026-04-10T12:15:35Z","snapshot_observed_at":"2026-08-13T11:18:25.874745Z","submitted_at":"2025-09-05T16:18:20Z","title":"BEDTime: A Unified Benchmark for Automatically Describing Time Series","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T18:55:18.730234Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2509.05215"},"observation_digest":"sha256:74ec19694deb8f5b5d253ac989057c94bf6dea54380cd354474cee30f9d9ddaa","observation_id":"fb803134-5866-44c5-aaa9-778e1102753f","resolution":{"observed_at":"2026-05-18T18:56:46.031145Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-04T16:49:34.939203Z","title":"Temporal data meets llm -- explainable financial time series forecasting, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11575","last_updated":"2026-06-10T07:42:17Z","snapshot_observed_at":"2026-08-17T22:09:51.150811Z","submitted_at":"2025-09-15T04:39:50Z","title":"A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models","version":3},"reference_index":136,"source":"arxiv_source","source_observed_at":"2026-08-04T16:49:34.939203Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2509.11575"},"observation_digest":"sha256:8113e5a7ed95812dbeb8fd2b488ea8af6b62f302882a5e8b9f9d74b36d47c35c","observation_id":"9369c4ff-2048-44ee-b33e-2734c8d50477","resolution":{"observed_at":"2026-08-04T16:49:34.939203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-03T08:34:27.285030Z","title":"Tem- poral data meets llm – explainable financial time series forecasting.arXiv preprint arXiv:2306.11025,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.16632","last_updated":"2026-07-28T02:55:52Z","snapshot_observed_at":"2026-08-16T06:16:38.036047Z","submitted_at":"2026-01-23T10:33:34Z","title":"Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting","version":6},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T08:34:27.285030Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2601.16632"},"observation_digest":"sha256:99fa7f59f3c9f6b7f7c145cc4e62a4432edbdb3411fc89386649ef8f8c0c9e5e","observation_id":"533a17ea-080c-4eef-baa4-7077e554f1e7","resolution":{"observed_at":"2026-08-03T08:34:27.285030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-13T09:28:30.950169Z","title":"Temporal data meets llm–explainable financial time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.06265","last_updated":"2026-06-18T07:01:21Z","snapshot_observed_at":"2026-08-16T10:50:12.174439Z","submitted_at":"2026-04-07T02:37:45Z","title":"SMT-AD: a scalable quantum-inspired anomaly detection approach","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T09:28:30.950169Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2604.06265"},"observation_digest":"sha256:a883816ecf67a1f6a1252939dcf8caccd0e6adab6968fc33db3790950ab707a9","observation_id":"6f4f5baf-e835-4b58-ab7d-d8f50e76faa3","resolution":{"observed_at":"2026-07-13T09:28:30.950169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2604.06266","last_updated":"2026-04-07T03:21:14Z","snapshot_observed_at":"2026-07-06T22:54:48.916799Z","submitted_at":"2026-04-07T03:21:14Z","title":"Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T20:06:01.233352Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2604.06266"},"observation_digest":"sha256:6c1281da317eb356146e188d2154cd00e3c420cc9b8dfe2e3962b132611938a7","observation_id":"af7d10ff-f208-4a4c-9647-f42e50c491a6","resolution":{"observed_at":"2026-05-10T22:15:48.786964Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2604.18500","last_updated":"2026-04-20T16:52:22Z","snapshot_observed_at":"2026-08-16T16:57:48.093719Z","submitted_at":"2026-04-20T16:52:22Z","title":"QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-10T03:06:14.794251Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2604.18500"},"observation_digest":"sha256:410350f78f5fd3159c08f90d6056f6ead8910d6f166d69984c257599ed9cac13","observation_id":"10c6563f-2460-443b-9b85-6d2f48fcf2e1","resolution":{"observed_at":"2026-05-11T12:46:02.761373Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2605.05211","last_updated":"2026-04-10T17:36:04Z","snapshot_observed_at":"2026-08-17T23:59:55.505125Z","submitted_at":"2026-04-10T17:36:04Z","title":"A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T17:05:40.178716Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2605.05211"},"observation_digest":"sha256:792bc34ef2286f24168e078160f59a68dbd5eefaeaaedab2e9fa9f30e6b279c2","observation_id":"955a9fe4-c4c0-4486-9c05-2eeb7af025ae","resolution":{"observed_at":"2026-05-11T07:35:59.135640Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2606.03137","last_updated":"2026-07-01T08:33:01Z","snapshot_observed_at":"2026-08-05T05:03:21.120024Z","submitted_at":"2026-06-02T04:26:01Z","title":"Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T10:24:30.660372Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2606.03137"},"observation_digest":"sha256:d8c2369bec20016b6dd0af2c75106ed34a25ff9beca61cc9f0161b0fe6f5df8a","observation_id":"4da09af0-eefc-4017-8b20-665b4a5a984b","resolution":{"observed_at":"2026-07-02T03:06:29.381365Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2306.11025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-07-02T23:17:29.022593Z","title":"Temporal data meets llm–explainable financial time series forecasting","venue":null,"work_id":"7472c9c0-7f8b-4ce1-8f70-58dd04266518","year":2023},"citing_paper":{"arxiv_id":"2606.03137","last_updated":"2026-07-01T08:33:01Z","snapshot_observed_at":"2026-08-05T05:03:21.120024Z","submitted_at":"2026-06-02T04:26:01Z","title":"Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-02T23:10:03.733636Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2606.03137"},"observation_digest":"sha256:6a90c1dd14d3a6a207f08f992792453709dc3f38346279f7f712eeaf9bf54b8d","observation_id":"14c61fec-f13c-419a-beab-be64c18e6e30","resolution":{"observed_at":"2026-07-02T23:17:29.024019Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11025","snapshot_observed_at":"2026-08-01T23:29:50.791663Z","title":"Temporal data meets LLM: Explainable financial time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15414","last_updated":"2026-07-16T19:38:23Z","snapshot_observed_at":"2026-08-15T15:02:31.752930Z","submitted_at":"2026-07-16T19:38:23Z","title":"AI Trading: Evaluating Large Language Models for Technical Market Analysis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T23:29:50.791663Z"},"links":{"cited_paper":"/paper/2306.11025","citing_paper":"/paper/2607.15414"},"observation_digest":"sha256:1b33aa32e3eaf6d7f7499988616e88f8cdab3f8b042c2bc82fe5fb35b7f431ae","observation_id":"3afe0a75-9a10-448f-89da-e7028b064efb","resolution":{"observed_at":"2026-08-01T23:29:50.791663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2306.11025/citation-record","integrity":"/paper/2306.11025/integrity","json":"/paper/2306.11025/citation-record.json","paper":"/paper/2306.11025"},"outbound":[],"paper":{"arxiv_id":"2306.11025","last_updated":"2023-06-19T15:42:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T17:22:43.506452Z","submitted_at":"2023-06-19T15:42:02Z","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2306.11025."}