{"as_of":"2026-08-15T14:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1309ff612c07cbe3b3e5045b677d6b2bd8313d093814fdc711eb9f96f1c18f63","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:25:47.680976Z","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":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T13:55:05.253473Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-08T17:40:15.595697Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16643","snapshot_observed_at":"2026-08-10T13:55:05.253473Z","title":"Timerag: Boosting llm time series forecasting via retrieval-augmented generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.15942","last_updated":"2025-01-27T10:40:38Z","snapshot_observed_at":"2026-08-15T01:44:37.925561Z","submitted_at":"2025-01-27T10:40:38Z","title":"TimeHF: Billion-Scale Time Series Models Guided by Human Feedback","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T13:55:05.253473Z"},"links":{"cited_paper":"/paper/2412.16643","citing_paper":"/paper/2501.15942"},"observation_digest":"sha256:e3d790bfea6302710f076ecea104b68c79181a3218099c84aef50c37420c77a0","observation_id":"fd5a2e0a-3af3-460a-8550-a7efd414d322","resolution":{"observed_at":"2026-08-10T13:55:05.253473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":"2412.16643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.16643","snapshot_observed_at":"2026-08-08T17:40:15.595697Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","venue":"cs.AI","work_id":"0c967eeb-dae5-48fb-84ed-b74e4384c443","year":2024},"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":5,"source":"pdf_text","source_observed_at":"2026-08-08T17:40:15.216866Z"},"links":{"cited_paper":"/paper/2412.16643","citing_paper":"/paper/2502.05878"},"observation_digest":"sha256:8ccb8d55daf7e793bb643642bba55e85218f8dcb0e7e8b2192f4d937000d7a3d","observation_id":"a74514c4-2072-423e-ab84-b443473b6aaa","resolution":{"observed_at":"2026-08-08T17:40:15.600811Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16643","snapshot_observed_at":"2026-08-04T16:49:34.913898Z","title":"Timerag: Boosting llm time series forecasting via retrieval-augmented generation, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11575","last_updated":"2026-06-10T07:42:17Z","snapshot_observed_at":"2026-08-06T03:43:03.418957Z","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":128,"source":"arxiv_source","source_observed_at":"2026-08-04T16:49:34.913898Z"},"links":{"cited_paper":"/paper/2412.16643","citing_paper":"/paper/2509.11575"},"observation_digest":"sha256:82d5e9cc2a4e077890dfd24b4bf06916f532a246234abc2a2a7d5ba8f83dad88","observation_id":"4d4e9ff3-b635-4c91-988b-7d2cb68d6460","resolution":{"observed_at":"2026-08-04T16:49:34.913898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.16643/citation-record","integrity":"/paper/2412.16643/integrity","json":"/paper/2412.16643/citation-record.json","paper":"/paper/2412.16643"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:25:48.819698Z","title":"Time series forecasting of petroleum production using deep lstm recurrent networks,","venue":null,"work_id":"545ca1ca-99b6-4674-b93d-55ad98575293","year":2019},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.429158Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:148c5e1d86d29e1e3f5c7b0a6963b99fb64917c18b8350ab4ece6fc5b032e240","observation_id":"813f302f-8b11-4652-beac-05880611cda0","resolution":{"observed_at":"2026-08-11T10:25:48.826569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04451","last_updated":"2020-02-18T16:01:18Z","snapshot_observed_at":"2026-07-06T08:50:12.690900Z","submitted_at":"2020-01-13T18:38:28Z","title":"Reformer: The Efficient Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04451","snapshot_observed_at":"2026-08-11T10:25:47.437972Z","title":"Reformer: The efficient transformer,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.437972Z"},"links":{"cited_paper":"/paper/2001.04451","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:94b2b8263a570a8bf491f60ed210a0847bdd818c84f10e67058831237050b215","observation_id":"a6e106b9-8144-4dd4-a0d9-aa56435fee24","resolution":{"observed_at":"2026-08-11T10:25:47.437972Z","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":"2026-08-11T10:25:47.447128Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.447128Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:a3a21ee4824ffcf08558c8831fbc188b1c9c7661e79b7d19e452082fdcecc493","observation_id":"4d55fa07-1c13-4ce1-bb66-5f0024700d3f","resolution":{"observed_at":"2026-08-11T10:25:47.447128Z","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":"2026-08-11T10:25:47.454896Z","title":"Time-series forecasting with deep learning: a survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.454896Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:011fe07ff8f7f23486964888f3942efe17b2ede60e24aa9761c01fefe9b3de23","observation_id":"d0070e73-0493-440d-a728-d2cd354524ee","resolution":{"observed_at":"2026-08-11T10:25:47.454896Z","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":"2026-08-11T10:25:47.466039Z","title":"A survey of time series foundation models: Generalizing time series representation with large language mode,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.466039Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:bb91ac629f79a3d2bedeec259a77687b36acc195e069f4086565b276752bcd59","observation_id":"dbed4ea5-a227-4f53-9dfc-aff3c4d200c6","resolution":{"observed_at":"2026-08-11T10:25:47.466039Z","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":"2026-08-11T10:25:48.758908Z","title":"Promptcast: A new prompt-based learning paradigm for time series forecasting,","venue":null,"work_id":"626be350-ea4a-4711-9f45-69c6d612b9a3","year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.481540Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:0e350c849a7e59e0ad9151ee2f47757a21b46407e4da1ec3c6d80ec3737d7ac6","observation_id":"1214d6f5-7d16-46ec-a42b-8f29c207b482","resolution":{"observed_at":"2026-08-11T10:25:48.768279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:47.490553Z","title":"One fits all: Power general time series analysis by pretrained lm,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.490553Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:dca7c011dbfe8a46a96389c5f06a01b84a78bc7027e834f2401d499a6df42429","observation_id":"f21dcfea-11c9-4ff0-88b4-4121dcf609ef","resolution":{"observed_at":"2026-08-11T10:25:47.490553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03182","last_updated":"2024-02-05T16:46:35Z","snapshot_observed_at":"2026-08-13T09:27:49.839216Z","submitted_at":"2024-02-05T16:46:35Z","title":"Empowering Time Series Analysis with Large Language Models: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03182","snapshot_observed_at":"2026-08-11T10:25:47.497652Z","title":"Empowering time series analysis with large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.497652Z"},"links":{"cited_paper":"/paper/2402.03182","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:9d364337320fc3ebd16c5257fb6acc13b91b9985e4f6a5b13b23b6de5413d689","observation_id":"4dee1d95-ccae-4215-a905-df2e7029b2ca","resolution":{"observed_at":"2026-08-11T10:25:47.497652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04023","last_updated":"2023-11-28T09:01:12Z","snapshot_observed_at":"2026-08-07T14:17:12.140094Z","submitted_at":"2023-02-08T12:35:34Z","title":"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04023","snapshot_observed_at":"2026-08-11T10:25:47.508484Z","title":"A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.508484Z"},"links":{"cited_paper":"/paper/2302.04023","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:e088f0c25c921aa2227728941879703937f8f1e5cfd7b92e7435a21669a3f4fc","observation_id":"7548d0a2-2196-4242-ada8-47b047a019b5","resolution":{"observed_at":"2026-08-11T10:25:47.508484Z","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":"2026-08-11T10:25:48.712244Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,","venue":null,"work_id":"80bf1bf8-2ff4-4b77-9083-79113b2d4f11","year":2020},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.515818Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:e7471a316c45002e034046725a590f163fabe3e41b31fcc8a634dfc126501669","observation_id":"45e2483d-a40d-471d-919f-3bcd501fbe9b","resolution":{"observed_at":"2026-08-11T10:25:48.718903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:47.523487Z","title":"Dynamic time warping,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.523487Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:84826c35c0662b3e8c6680a05595329e7aa8358a64f7c84e38990b3263952f4e","observation_id":"84fd8feb-1a6a-48d5-82e1-33c215a6ebe9","resolution":{"observed_at":"2026-08-11T10:25:47.523487Z","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":"2026-08-11T10:25:47.534510Z","title":"The m4 competition: 100,000 time series and 61 forecasting methods,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.534510Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:81e07afe8cde3e76cb69250731f1a6e10e885d0de5d413e46f9033dcc7c44b38","observation_id":"5697a571-cef1-4f64-a335-a8c00ddeb07a","resolution":{"observed_at":"2026-08-11T10:25:47.534510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-13T07:00:35.291225Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-08-11T10:25:47.545713Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.545713Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:8dacd7032ff7339a9a8d4b9f4315cf48d2e4fa9e6b0e8f67125638ea5bbe7dc5","observation_id":"3527b177-b2bb-444d-915d-51c05b54b747","resolution":{"observed_at":"2026-08-11T10:25:47.545713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-08-14T11:34:28.022352Z","submitted_at":"2024-03-12T16:53:54Z","title":"Chronos: Learning the Language of Time Series","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07815","snapshot_observed_at":"2026-08-11T10:25:47.555139Z","title":"Chronos: Learning the Language of Time Series,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.555139Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:7073ab6c0418433fe074661be63296fa90d33081009c35f00702c9c885a08021","observation_id":"982d923e-521b-416d-b0b4-f6abdd9052dd","resolution":{"observed_at":"2026-08-11T10:25:47.555139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05165","last_updated":"2022-11-09T19:33:27Z","snapshot_observed_at":"2026-08-13T13:45:46.560625Z","submitted_at":"2022-11-09T19:33:27Z","title":"Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database","version":1},"cited_work":{"arxiv_id":"2211.05165","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.05165","snapshot_observed_at":"2026-08-11T10:25:47.800143Z","title":"Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database","venue":"cs.CL","work_id":"e82fbff3-6cbc-4064-8654-f366dad2f8d6","year":2022},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.567583Z"},"links":{"cited_paper":"/paper/2211.05165","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:e7c9660b046e57e7e7cda5fbaee582fe64323222dc59cbcda6312b2deac02cd4","observation_id":"3d69f0a1-61a6-4732-92b1-862628f7cafc","resolution":{"observed_at":"2026-08-11T10:25:47.812772Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.639416Z","title":"Lost in the middle: How language models use long contexts,","venue":null,"work_id":"b4aa4d52-019a-4c8c-a040-79575bc2bce3","year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.575650Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:7fa8c8fa10bdb86cd9579a07ed7de3e80466e70f1b520bb90d4285df2b0a01a7","observation_id":"751d020b-7ecd-4a57-a289-f0d534ef0193","resolution":{"observed_at":"2026-08-11T10:25:48.651365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.605384Z","title":"LSTPrompt: Large language models as zero-shot time series forecasters by long-short-term prompting,","venue":null,"work_id":"c48740ad-414e-456d-b905-f8c37d4c6881","year":2024},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.581257Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:46a14007e7cc48160526a9a35d167ab3a165225f0df7079e1acc37aebbba072b","observation_id":"cf38dce3-2b1d-4647-9680-364eae481d75","resolution":{"observed_at":"2026-08-11T10:25:48.616245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:47.589360Z","title":"Dynamic programming algorithm optimiza- tion for spoken word recognition,","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.589360Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:766b3c9cb39ed245fe2d1e6aa2cd431df2cd205447875102b9ad134815b7ae8e","observation_id":"3ef6e5bb-516a-476e-abeb-4c61fe5fd817","resolution":{"observed_at":"2026-08-11T10:25:47.589360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10437","last_updated":"2020-02-20T21:08:57Z","snapshot_observed_at":"2026-08-14T16:26:52.986402Z","submitted_at":"2019-05-24T20:28:57Z","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10437","snapshot_observed_at":"2026-08-11T10:25:47.597085Z","title":"N- beats: Neural basis expansion analysis for interpretable time series forecasting,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.597085Z"},"links":{"cited_paper":"/paper/1905.10437","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:66d3b9e52f331d8f148b3ced0d9629011c7269a8ae184be6ad4fc9a3187109a2","observation_id":"f4033238-1d11-40c5-962b-16d6bdfac2e0","resolution":{"observed_at":"2026-08-11T10:25:47.597085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06625","last_updated":"2024-03-14T11:45:57Z","snapshot_observed_at":"2026-08-14T20:15:49.960714Z","submitted_at":"2023-10-10T13:44:09Z","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06625","snapshot_observed_at":"2026-08-11T10:25:47.607022Z","title":"itransformer: Inverted transformers are effective for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.607022Z"},"links":{"cited_paper":"/paper/2310.06625","citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:58e8a87baade0a253c860bb045faf25e5fa0f2c417786675e66aa3031608fbdb","observation_id":"7ad97caf-7d28-41b7-a0ef-6127ea40d3c6","resolution":{"observed_at":"2026-08-11T10:25:47.607022Z","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":"2026-08-11T10:25:48.525200Z","title":"Fed- former: Frequency enhanced decomposed transformer for long-term series forecasting,","venue":null,"work_id":"3ccff5ee-baff-41cc-b0fe-3e5610340209","year":2022},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.614295Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:e613f0a7a45185896a5c77b1bf58185afb47d1adc04e466675f73d715a6727f4","observation_id":"cec97eb9-56e1-4555-8993-0709f43e0b52","resolution":{"observed_at":"2026-08-11T10:25:48.565123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.493592Z","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,","venue":null,"work_id":"d1707701-350f-43ef-868e-1d0fdf19b698","year":2021},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.623110Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:472bcfb6b99946a685e033b70e8514fee195732dcead8a422f93d2d96ed80086","observation_id":"ecb84f0b-4966-4c60-81da-6ce85041ffed","resolution":{"observed_at":"2026-08-11T10:25:48.507703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.468005Z","title":"Autoformer: Searching transformers for visual recognition,","venue":null,"work_id":"c3c2b0fc-6a14-4807-a85d-5de4495d6dae","year":2021},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.632434Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:b3f2dd050decb550115f593394bd9efb61639ccd985a338af8c0ad0ef5c18574","observation_id":"7c466a99-cdfe-4f0a-a087-82113ca83e46","resolution":{"observed_at":"2026-08-11T10:25:48.475250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:47.639264Z","title":"Are transformers effective for time series forecasting?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.639264Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:e5244f621c40860fa145b05fc82c85e2010f9de232d6e5462d7508c2d3ab8908","observation_id":"93c1a692-1c3e-47ae-b62f-1b0ce913ea94","resolution":{"observed_at":"2026-08-11T10:25:47.639264Z","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":"2026-08-11T10:25:48.397845Z","title":"Tsmixer: Lightweight mlp-mixer model for multivariate time series forecasting,","venue":null,"work_id":"fcfe1d71-059b-4974-b9d8-5290149f32ef","year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.646289Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:56d53496f6fe349e6f303aee4216626e0e1556d78fc10f796ce18d2f7d2758e5","observation_id":"22db6b79-f6b4-4bab-a444-9baf25311ed4","resolution":{"observed_at":"2026-08-11T10:25:48.411224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.376191Z","title":"Micn: Multi-scale local and global context modeling for long-term series forecasting,","venue":null,"work_id":"429a14c7-7d38-4e4d-b1bd-1cc6412dee09","year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.664062Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:76275506a0c8b9678bd8ee297b5c407ab467a3511b745b7b3221fe7195480c95","observation_id":"e5574f6e-cd26-43c3-b761-f7c69312f1da","resolution":{"observed_at":"2026-08-11T10:25:48.385268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.351354Z","title":"Film: Frequency improved legendre memory model for long-term time series forecasting,","venue":null,"work_id":"5fc51d59-bab1-40cf-9a22-507223f854f0","year":2022},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.670947Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:2fc2c3fccbd85696a9a769706d1ba8a2cbe0ffa503c8c80d78d551eaa1c95864","observation_id":"695a0fb2-58ef-490e-974b-617887dce686","resolution":{"observed_at":"2026-08-11T10:25:48.361656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T10:25:48.324773Z","title":"Lightts: Lightweight time series classification with adaptive ensemble distillation,","venue":null,"work_id":"bb843356-ab98-47a9-9cb3-e7712d2aab3f","year":2023},"citing_paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:25:47.680976Z"},"links":{"citing_paper":"/paper/2412.16643"},"observation_digest":"sha256:9322a25617ecb4301d2a2a3584a2dc29b936c494f96ccc9ce96192a6051553e7","observation_id":"4e241ee0-f8f4-4c62-9091-5a1361b26a9a","resolution":{"observed_at":"2026-08-11T10:25:48.332663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16643","last_updated":"2024-12-21T14:27:38Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-14T20:17:33.829102Z","submitted_at":"2024-12-21T14:27:38Z","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":12},"total_outbound_references":28},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 3 inbound Pith citation observations for arXiv:2412.16643."}