{"as_of":"2026-08-22T05:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e507354c5a9f7b761cf9bb449088093173943e76bf26153973493caea67fdcae","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:24:18.697041Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:37:53.815881Z","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":"2026-08-06T17:37:54.801817Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_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},"cited_work":{"arxiv_id":"2412.02525","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.02525","snapshot_observed_at":"2026-08-06T17:37:54.801817Z","title":"LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data","venue":"cs.LG","work_id":"b3e8ed02-02a6-4d9e-bb9a-6cd00e738485","year":2024},"citing_paper":{"arxiv_id":"2507.10349","last_updated":"2025-07-14T14:51:24Z","snapshot_observed_at":"2026-08-18T14:37:16.713921Z","submitted_at":"2025-07-14T14:51:24Z","title":"TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:37:53.815881Z"},"links":{"cited_paper":"/paper/2412.02525","citing_paper":"/paper/2507.10349"},"observation_digest":"sha256:8481d7eb87154c72b35c9ae4b85dedd454ef472603107f8fa69e883f42f9e7ae","observation_id":"d2d13c89-cffc-4d38-84ff-1b3208100738","resolution":{"observed_at":"2026-08-06T17:37:54.873654Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02525/citation-record","integrity":"/paper/2412.02525/integrity","json":"/paper/2412.02525/citation-record.json","paper":"/paper/2412.02525"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:18.617264Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"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":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.617264Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:1e52c391069b36789922bebd237a24915667b374affa06d9e692d0f14d2c6fc4","observation_id":"1d5a3363-50fe-4ea6-bb1e-a0e1208ed4b6","resolution":{"observed_at":"2026-08-11T23:24:18.617264Z","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":"2026-08-11T23:24:18.907131Z","title":"Deep learning with long short-term memory networks for financial market predictions","venue":null,"work_id":"708c1577-38b1-45e2-90ae-3319ee830dc6","year":2018},"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":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.621361Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:3eafb65d20b3006934dd73691e7c4a6d738e377c3362a97c85f4c1388762db9e","observation_id":"aae823c2-9df1-497e-bf0c-48f18f11f06f","resolution":{"observed_at":"2026-08-11T23:24:18.910758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T23:24:18.624441Z","title":"Backpropagation applied to handwritten zip code recognition","venue":null,"work_id":null,"year":1989},"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":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.624441Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:35c20db55c9fa90c994e6b8bad7f5b719f4ca59d56737ea801aa44e4002a7c08","observation_id":"71a07151-4907-4fad-b17e-e9e3052de754","resolution":{"observed_at":"2026-08-11T23:24:18.624441Z","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":"2026-08-11T23:24:18.628074Z","title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","venue":null,"work_id":null,"year":2021},"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":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.628074Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:dbb29eaba6792ddbf5efa46024ba75b14f283691763048e88c97de1ee7ad2a90","observation_id":"9c2fee53-0f45-4e0f-acb1-ce78d0be8025","resolution":{"observed_at":"2026-08-11T23:24:18.628074Z","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":"2026-08-11T23:24:18.631174Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","venue":null,"work_id":null,"year":2021},"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":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.631174Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:fdc966ef2f4ad0065705fdf5091a25b4ad7a284fffb49aac745335192f9f7357","observation_id":"2193fbac-2a83-4954-bbe0-30721e0e6dae","resolution":{"observed_at":"2026-08-11T23:24:18.631174Z","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":"2026-08-11T23:24:18.634142Z","title":"Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting","venue":null,"work_id":null,"year":2021},"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":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.634142Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:168f1cbcbf5d52a57efc67ddd769d4900d75cd9e34f2e192a25bad22c6375234","observation_id":"17be3fc1-2ae5-40cf-b4fa-a89ae1d441f3","resolution":{"observed_at":"2026-08-11T23:24:18.634142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-08-17T01:22:50.943392Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-11T23:24:18.637471Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2022},"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":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.637471Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:69f6d1ffc6134d9389f3fdebd3c45540ecb46a6a73fe42266fc1dc4a5b168127","observation_id":"a92f14d9-cbeb-40fd-9dc1-fa40b79def4f","resolution":{"observed_at":"2026-08-11T23:24:18.637471Z","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":"2026-08-11T23:24:18.877040Z","title":"A temporal fusion transformer for short-term freeway traffic speed multistep prediction","venue":null,"work_id":"8b0c509f-322f-4151-b58d-7a650779295f","year":2022},"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":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.640557Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:bdaa3407382733b29ec862308961031c0cb8bcdc3962ad1813f383cfdfe81c24","observation_id":"935d9ff0-5ffa-4aa1-877e-0f12bab99bde","resolution":{"observed_at":"2026-08-11T23:24:18.880293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T23:24:18.868426Z","title":"Temporal fusion transformers model for traffic flow prediction","venue":null,"work_id":"1a6b0aa1-6cd1-4b83-8db4-3ac022390c37","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":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.643158Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:f1a1c413ff8b9f895a385cafd0adf271e1c947f4b80f2180154ba25eb032a7ae","observation_id":"f7025825-f4c3-4119-a2d1-19a7dc35f2e0","resolution":{"observed_at":"2026-08-11T23:24:18.871803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14799","last_updated":"2022-01-27T03:02:39Z","snapshot_observed_at":"2026-08-16T19:16:46.567629Z","submitted_at":"2020-09-30T17:12:46Z","title":"MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14799","snapshot_observed_at":"2026-08-11T23:24:18.645986Z","title":"Mqtransformer: Multi-horizon forecasts with context dependent and feedback-aware attention","venue":null,"work_id":null,"year":2009},"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":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.645986Z"},"links":{"cited_paper":"/paper/2009.14799","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:64c70f639d9c11c591dcc00fea1e07b390c3ee82ad9bda5a96a877c098259ae9","observation_id":"c10ebe44-6366-47c5-8853-244dac7b1f14","resolution":{"observed_at":"2026-08-11T23:24:18.645986Z","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":"2026-08-11T23:24:18.860112Z","title":"Deepar: Probabilis- tic forecasting with autoregressive recurrent networks","venue":null,"work_id":"25678fda-d504-4b06-8a0b-c39903d1cd92","year":2020},"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":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.648983Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:8f03d0312b0d47b0d3929d20a5bbb0388767df5e2602bc95bc464f47cd4c724e","observation_id":"3b9e8a42-cf7b-4647-a55f-367828e2980a","resolution":{"observed_at":"2026-08-11T23:24:18.863093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T23:24:18.851608Z","title":"Asset bundling for hierarchical forecasting of wind power generation","venue":null,"work_id":"c981ba3e-eb2f-4cb4-81f2-e6767efce686","year":2024},"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":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.652025Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:e2b8a7030ceb941977758f65af8357c1467b9553e0ecbff6c3e67b2b325ff83c","observation_id":"3682ae13-e97f-4682-a3a4-b0a4c87e97de","resolution":{"observed_at":"2026-08-11T23:24:18.854748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T23:24:18.842544Z","title":"Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture","venue":null,"work_id":"07eb5f80-ffa6-4128-9d94-37a1902d6ce4","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":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.655388Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:6589561b89068430f9cc468b5b285375db32193959832f0eeeb6d2e3614226be","observation_id":"105b7248-251c-4197-8f0a-b5aa5aa86630","resolution":{"observed_at":"2026-08-11T23:24:18.845886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T23:24:18.834727Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis, 2023","venue":null,"work_id":"0040883c-7932-4f89-9f9c-5d5c38f9744e","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":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.658217Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:cb14eb5ab9e4aaf5d3c48c922fdf74b470ca230cf9d2831c0d77fb323345934e","observation_id":"585ced4a-f6ea-4cfd-83ad-98aa7ac6b1c9","resolution":{"observed_at":"2026-08-11T23:24:18.837761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-08-20T06:56:18.647581Z","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":"2026-08-11T23:24:18.661182Z","title":"Chronos: Learning the language of time series","venue":null,"work_id":null,"year":2024},"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":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.661182Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:71c02e2d514ca177b347c9e462cc5bb0e51e3020e443acaf2c0efe5caee533c6","observation_id":"16434a6f-7f6c-40fe-ac54-59c54a1d0669","resolution":{"observed_at":"2026-08-11T23:24:18.661182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.11053","last_updated":"2018-06-28T17:54:39Z","snapshot_observed_at":"2026-08-19T19:31:06.397853Z","submitted_at":"2017-11-29T19:01:32Z","title":"A Multi-Horizon Quantile Recurrent Forecaster","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.11053","snapshot_observed_at":"2026-08-11T23:24:18.664291Z","title":"A multi- horizon quantile recurrent forecaster","venue":null,"work_id":null,"year":2017},"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":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.664291Z"},"links":{"cited_paper":"/paper/1711.11053","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:e0d0089c663fd39165db55b794d81405d15a7439e1fc2d522107780f155fe937","observation_id":"017ef9bf-33d8-4501-b063-773d4c8b49a3","resolution":{"observed_at":"2026-08-11T23:24:18.664291Z","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":"2026-08-11T23:24:18.825894Z","title":"Large language models are zero-shot time series forecasters","venue":null,"work_id":"476b9271-fa14-4346-9490-6b1582ff0169","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":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.667299Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:f979b2a2813a782de9ee4c41ffcbb2203ad8f645a3d12ca21d60131b5bbb3c52","observation_id":"2abfdf85-beb5-490d-9afa-9ed1338df350","resolution":{"observed_at":"2026-08-11T23:24:18.829652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03182","last_updated":"2024-02-05T16:46:35Z","snapshot_observed_at":"2026-08-16T14:21:21.465222Z","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":"2026-08-11T23:24:18.670038Z","title":"Empowering time series analysis with large language models: A survey","venue":null,"work_id":null,"year":2024},"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":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.670038Z"},"links":{"cited_paper":"/paper/2402.03182","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:8626c86e75ddf70331d5757cce6a97c0da64cd1e067c9b338fbcd5c70ebefb8b","observation_id":"e2d3866f-205d-4e97-a8b5-e36f2488312e","resolution":{"observed_at":"2026-08-11T23:24:18.670038Z","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":"2026-08-21T18:52:46.573543Z","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:ac2d0f7b5c4770b5581057718ecd5043ed16fd2bdbe9f9639e617137d5236909","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":"2308.15197","last_updated":"2024-01-09T14:08:03Z","snapshot_observed_at":"2026-08-16T15:04:59.451614Z","submitted_at":"2023-08-29T10:24:23Z","title":"Where Would I Go Next? Large Language Models as Human Mobility Predictors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.15197","snapshot_observed_at":"2026-08-11T23:24:18.676524Z","title":"Where would i go next? large language models as human mobility predictors","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":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.676524Z"},"links":{"cited_paper":"/paper/2308.15197","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:8f8cd17fb8f048a3e87c75e0596bdf34b008921ae79ba6f8b67c02c3fae616e3","observation_id":"de630364-348d-4f2a-9fb5-d846063919f5","resolution":{"observed_at":"2026-08-11T23:24:18.676524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15525","last_updated":"2023-05-24T19:25:16Z","snapshot_observed_at":"2026-08-19T21:50:00.488815Z","submitted_at":"2023-05-24T19:25:16Z","title":"Large Language Models are Few-Shot Health Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15525","snapshot_observed_at":"2026-08-11T23:24:18.679377Z","title":"Large language models are few-shot health learners","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":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.679377Z"},"links":{"cited_paper":"/paper/2305.15525","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:bcc20ef0a95c43ce5e8dd5e856b9782a433334db47694421d04100ab50c680f4","observation_id":"6dda853e-857a-4eff-9526-10e986a490f9","resolution":{"observed_at":"2026-08-11T23:24:18.679377Z","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":"2026-08-11T23:24:18.682185Z","title":"Promptcast: A new prompt-based learning paradigm for 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":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.682185Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:94f9fb2239d84e00b5d8e11bf8fa1056ca0cf40c5c0ab5822f9f20816ec6c2c2","observation_id":"0a71499d-765a-484a-9661-97bb4069d18a","resolution":{"observed_at":"2026-08-11T23:24:18.682185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08469","last_updated":"2025-02-20T16:48:08Z","snapshot_observed_at":"2026-08-20T07:20:32.320635Z","submitted_at":"2023-08-16T16:19:50Z","title":"LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08469","snapshot_observed_at":"2026-08-11T23:24:18.685200Z","title":"Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms","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":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.685200Z"},"links":{"cited_paper":"/paper/2308.08469","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:dab2d1a21e8131b3ef08b23d4789c92544cf0e5534df10058339a285075e1c20","observation_id":"bafed35f-5b54-4642-8da9-ab81abfa86b7","resolution":{"observed_at":"2026-08-11T23:24:18.685200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-18T14:31:02.753428Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-11T23:24:18.688066Z","title":"Tempo: Prompt-based generative pre-trained transformer for 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":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.688066Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:d66fd7755aa920bea4910ab226ea397c2a4722b917cbaa787366372edcd75984","observation_id":"cfa60f85-73ba-472a-88e3-b76a5b2da543","resolution":{"observed_at":"2026-08-11T23:24:18.688066Z","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":"2026-08-11T23:24:18.811414Z","title":null,"venue":null,"work_id":"88c2629c-5bb0-422e-9aea-a1e504b3f0ce","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":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.691241Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:22258604c2dd4092f621708e2f0bac0a10656328a35fded1e79b27e7de920e1b","observation_id":"f649938c-1c5c-48e3-add8-52646b8fcd2f","resolution":{"observed_at":"2026-08-11T23:24:18.814690Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-20T11:47:17.477107Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T23:24:18.694214Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"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":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.694214Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:c9fb2a9a7b7d122f6be24fc01ad44236576534191c06f92542258dac58d280d2","observation_id":"265fa3a5-7938-478e-a3fe-18dde1e19465","resolution":{"observed_at":"2026-08-11T23:24:18.694214Z","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":"2026-08-11T23:24:18.801613Z","title":"Holiday-Encoding Prompt","venue":null,"work_id":"efb058ed-e949-4b65-bb00-0577a69562b0","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":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:18.697041Z"},"links":{"citing_paper":"/paper/2412.02525"},"observation_digest":"sha256:70e34dacfe9d36947384bb21ec8ec36542ee4df05fd087a33386773b8ae2008b","observation_id":"9dad5ba8-d296-4680-a397-f0f0c9cae38c","resolution":{"observed_at":"2026-08-11T23:24:18.805384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.02525","last_updated":"2024-12-03T16:18:42Z","latest_version":1,"primary_category":"cs.LG","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"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":27},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.02525."}