{"as_of":"2026-08-04T22:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73e6c59866547b2a2fb2c6036ab41419c759a2359d43277b154b0a2aac207bb4","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":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T11:15:44.767231Z","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":"2026-07-04T13:19:50.478446Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2407.13278","last_updated":"2026-05-04T08:07:42Z","snapshot_observed_at":"2026-07-06T18:48:20.902620Z","submitted_at":"2024-07-18T08:31:55Z","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","version":3},"reference_index":155,"source":"pdf_text","source_observed_at":"2026-05-23T23:03:45.096751Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2407.13278"},"observation_digest":"sha256:2ac3bf7afeabf483401e1a5efa423d7263b89988891f16b099bc224bedc49e50","observation_id":"ef481ea8-aefb-4f68-a3fc-98d48ef4a0a4","resolution":{"observed_at":"2026-05-23T23:05:51.453718Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2509.15105","last_updated":"2026-05-22T12:07:12Z","snapshot_observed_at":"2026-08-04T04:40:44.681486Z","submitted_at":"2025-09-18T16:11:31Z","title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-25T08:22:24.238459Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2509.15105"},"observation_digest":"sha256:be3afb2c1c8d1693a57d6755a8fd9a2ffe53ab9a6b83a11e4d5991404bea3911","observation_id":"4c3b9edc-43f7-4e1e-b413-6170ed3d6ae4","resolution":{"observed_at":"2026-05-25T08:25:34.132017Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2510.06063","last_updated":"2026-05-27T22:16:59Z","snapshot_observed_at":"2026-08-04T11:15:40.604166Z","submitted_at":"2025-10-07T15:54:34Z","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-21T20:23:40.207908Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2510.06063"},"observation_digest":"sha256:4e6a19b6f9e48f5305d0bbb19169dfe7e647e36a9dce5aee55038a677d6d3b53","observation_id":"2b7f0dd3-d728-4c3b-a825-926f668ca230","resolution":{"observed_at":"2026-05-21T20:24:21.568593Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-08-04T11:15:44.767231Z","title":"Timer: Generative pre-trained transformers are large time series models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.06063","last_updated":"2026-05-27T22:16:59Z","snapshot_observed_at":"2026-08-04T11:15:40.604166Z","submitted_at":"2025-10-07T15:54:34Z","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T11:15:44.767231Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2510.06063"},"observation_digest":"sha256:9f2f5edd4178d17bf175a9c5b543bf32da469b7a4b1265e6c2785afb7cadde96","observation_id":"218c7587-de84-4637-9a81-00f260e3a254","resolution":{"observed_at":"2026-08-04T11:15:44.767231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-08-04T06:03:32.284798Z","title":"arXiv preprint arXiv:2402.02368 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18662","last_updated":"2026-08-01T12:13:31Z","snapshot_observed_at":"2026-08-04T21:22:01.817584Z","submitted_at":"2026-02-20T23:47:55Z","title":"Large Causal Models for Temporal Causal Discovery","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T06:03:32.284798Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2602.18662"},"observation_digest":"sha256:584aa9e0de1d5e6a6005040e68db99d2532ab2dc42612f5c74a79f2a1f5c72c4","observation_id":"96542447-41f0-4a57-8713-e11d81459df3","resolution":{"observed_at":"2026-08-04T06:03:32.284798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:5bfa5135a977eed88615d6b799d7d14485f3427c3e4e46e52086402092a11753","observation_id":"2a25ec38-0d88-40e2-ba3d-2009aeaf3e16","resolution":{"observed_at":"2026-05-11T20:36:09.690612Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2605.01418","last_updated":"2026-05-02T12:30:52Z","snapshot_observed_at":"2026-07-06T23:14:38.379875Z","submitted_at":"2026-05-02T12:30:52Z","title":"TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-09T14:19:12.559496Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2605.01418"},"observation_digest":"sha256:2dd1235220c0e6c92c31efdc22c9be38221da7ee80ab1ac9a7a568c4432e331a","observation_id":"f3468a77-6077-474d-b90c-47fa7e658ea7","resolution":{"observed_at":"2026-05-11T17:01:07.982047Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2605.10038","last_updated":"2026-05-11T06:09:17Z","snapshot_observed_at":"2026-08-02T18:58:33.997806Z","submitted_at":"2026-05-11T06:09:17Z","title":"TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-12T02:50:22.377716Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2605.10038"},"observation_digest":"sha256:fe61806a05e1dc352776ffc4067464f8a2e9ea66b5d74ae4d6812acce68443df","observation_id":"d0c8f095-9896-4958-82ee-f962459fb78e","resolution":{"observed_at":"2026-05-12T02:51:17.584562Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2605.17340","last_updated":"2026-05-19T02:26:50Z","snapshot_observed_at":"2026-07-06T23:28:21.395050Z","submitted_at":"2026-05-17T09:19:22Z","title":"Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-20T14:42:04.841976Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2605.17340"},"observation_digest":"sha256:5fcf0e067194263e1ce8d4780b04f96e0741d6597fb1c376b750f1cc7ddcd51d","observation_id":"9946d7b7-78b9-417e-b097-b1d181923dd0","resolution":{"observed_at":"2026-05-20T14:43:22.438569Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2606.05481","last_updated":"2026-06-03T22:00:29Z","snapshot_observed_at":"2026-08-02T18:59:33.687986Z","submitted_at":"2026-06-03T22:00:29Z","title":"Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T06:42:59.183555Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2606.05481"},"observation_digest":"sha256:bf0281210a6e98fc7ebbf0e9e633f6d4e3a1e2629b5e0f7023185c4b4afdc348","observation_id":"661c1b5c-a7f7-471a-952f-dd7854dc07d2","resolution":{"observed_at":"2026-07-02T07:46:46.107949Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2606.06285","last_updated":"2026-06-04T15:25:03Z","snapshot_observed_at":"2026-08-03T00:27:15.574186Z","submitted_at":"2026-06-04T15:25:03Z","title":"TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T01:50:24.426751Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2606.06285"},"observation_digest":"sha256:fdccbacdf3b182251ffdd5924dd175d9109b1bd0072320d3bc5eb851d70eef86","observation_id":"6c588817-7941-4fb9-957d-7a77059fb351","resolution":{"observed_at":"2026-07-02T12:46:56.980253Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2606.08630","last_updated":"2026-06-07T13:50:09Z","snapshot_observed_at":"2026-08-02T23:22:19.832178Z","submitted_at":"2026-06-07T13:50:09Z","title":"Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T18:52:56.379712Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2606.08630"},"observation_digest":"sha256:65ce3167af25351bc4dad08fb0b9c0998cf4492263736a3fb9731af7a5477e48","observation_id":"eb865cb4-4387-4a4a-9f8a-41cbd3f76d17","resolution":{"observed_at":"2026-07-02T22:27:26.138376Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2606.09954","last_updated":"2026-06-08T10:51:56Z","snapshot_observed_at":"2026-08-02T18:47:19.989988Z","submitted_at":"2026-06-08T10:51:56Z","title":"Does Normalization Choice Matter for Causal Large Time-Series Models?","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T17:26:01.947965Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2606.09954"},"observation_digest":"sha256:a457efe4d0752fc868e753206b9b3a9abb753ec0bc53b40bde4d166db72fa225","observation_id":"67bfb209-2160-468a-8865-89e57239767f","resolution":{"observed_at":"2026-07-03T00:07:28.573516Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2606.26549","last_updated":"2026-06-25T02:49:08Z","snapshot_observed_at":"2026-07-07T00:00:51.779266Z","submitted_at":"2026-06-25T02:49:08Z","title":"PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T05:22:10.800685Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2606.26549"},"observation_digest":"sha256:a839737f961ce9e9ea5cd337b54f8ce0a984b20fdc958e9987f4b315615f009e","observation_id":"747f5123-0459-4a72-838f-2ba75b96be5c","resolution":{"observed_at":"2026-07-04T13:19:50.479908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2607.01966","last_updated":"2026-07-02T09:58:46Z","snapshot_observed_at":"2026-08-02T13:51:43.739130Z","submitted_at":"2026-07-02T09:58:46Z","title":"Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-03T17:23:35.304926Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2607.01966"},"observation_digest":"sha256:38e883ec46e8c89f8aebf2ff0aaf676f77ed67433d65dfef0ebda4f57d05c049","observation_id":"49615edb-bf99-4e75-af81-aa4f6770a13b","resolution":{"observed_at":"2026-07-03T17:28:44.089312Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02368/citation-record","integrity":"/paper/2402.02368/integrity","json":"/paper/2402.02368/citation-record.json","paper":"/paper/2402.02368"},"outbound":[],"paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models"},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2402.02368."}