{"as_of":"2026-08-18T21:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f90255eb93e6718ccd934c768fe80dd3f9f49bb1df55140aa434edadb2c0af3","coverage":[{"denominator":155,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:09:38.023988Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.13262/citation-record","integrity":"/paper/2608.13262/integrity","json":"/paper/2608.13262/citation-record.json","paper":"/paper/2608.13262"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:37.412299Z","title":"Proceedings of the 41st International Conference on Machine Learning , articleno =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.412299Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:3b69850ae83366377fcf0bae80d7ea5c80ba90baf5bc01b1436475a1a7cf471a","observation_id":"cf58ac27-e2da-43b8-bff3-a935c0b841a1","resolution":{"observed_at":"2026-08-14T15:09:37.412299Z","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":"2026-08-14T15:09:37.417234Z","title":"Proceedings of the 41st International Conference on Machine Learning , articleno =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.417234Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:3c98da5a6227ea98f58b75cb9fe505224953995dd694bf0bbb4da4a2121ffec1","observation_id":"91b8339f-a06a-49bc-9582-86cdfacf3580","resolution":{"observed_at":"2026-08-14T15:09:37.417234Z","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":"2026-08-14T15:09:37.427201Z","title":"IEEE Transactions on Knowledge and Data Engineering , volume=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.427201Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:8176ebba81d7f3d9fa0e83aac79d6e27971c7f49a3f263089b78619a55059535","observation_id":"ace009ac-f538-4cb0-b7a9-efabe05989bd","resolution":{"observed_at":"2026-08-14T15:09:37.427201Z","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":"2026-08-14T15:09:37.432286Z","title":"Proceedings of the 19th International Symposium on Spatial and Temporal Data , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.432286Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:57736411270d2f27aa50b6199e6a6cce2c805e351ce97fd35141523697aac6eb","observation_id":"3f3a4436-d587-45cb-ae60-bdfcbc7d9cc3","resolution":{"observed_at":"2026-08-14T15:09:37.432286Z","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":"2026-08-14T15:09:37.436676Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.436676Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:ea7e721e4813c9205f378ac85bc69d95e7cebfe2860fb0613df2caa221150046","observation_id":"34802356-f02a-4808-ba99-b72278c82089","resolution":{"observed_at":"2026-08-14T15:09:37.436676Z","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":"2026-08-14T15:09:37.441124Z","title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.441124Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:89c05a562a78809efd6f4c52446f143fc79608bdcbb30027ad22676f0f33b568","observation_id":"7882fa2a-0d8a-4f40-bd2a-463aea046f29","resolution":{"observed_at":"2026-08-14T15:09:37.441124Z","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":"2026-08-14T15:09:37.445616Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.445616Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:19dcafc533a112b440d7389147e469dad49a3d8901f88357d318ebca68bdf395","observation_id":"93e16fe7-79a3-4279-ae67-1d32617d9cd4","resolution":{"observed_at":"2026-08-14T15:09:37.445616Z","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":"2026-08-14T15:09:37.449773Z","title":"Scientific reports , volume=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.449773Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:cda2c2de8535226f8ee97a50ce86b4b1bfa6e7ee157fcc1f230eb29146963fe3","observation_id":"3ca4ea89-500a-4a28-ac0c-4780535745fd","resolution":{"observed_at":"2026-08-14T15:09:37.449773Z","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":"2026-08-14T15:09:37.453914Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.453914Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e8d7afe86c739ca5d8f811a8ebb699098d006b6e6573c825a5cb96ecbf45d4bc","observation_id":"119f3b7c-c184-41d7-b7ca-2d91871a4e1d","resolution":{"observed_at":"2026-08-14T15:09:37.453914Z","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":"2026-08-14T15:09:37.493337Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.493337Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:f9aa468d297b50fd4abf5d7f3d4824835336638df5dccd0e9a2b2d0111c9c3dd","observation_id":"80946300-1356-4852-a428-e0dd01f9bb2d","resolution":{"observed_at":"2026-08-14T15:09:37.493337Z","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":"2026-08-14T15:09:37.497422Z","title":"Proceedings of the 41st International Conference on Machine Learning , articleno =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.497422Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:8b15a81fdf19cc2a544e4b256692158badeebed8d8cb8425fc9743bcbcb50f45","observation_id":"dedb34d5-8f15-4142-b96e-792e9ef32f69","resolution":{"observed_at":"2026-08-14T15:09:37.497422Z","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":"2026-08-14T15:09:37.502485Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.502485Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:3b89879a313ff9dc3ab578646cc820612fc5771ead3665d6fcd48c5d7e2b39d2","observation_id":"ad33c031-34bc-440a-b1cf-79e8a2a0a34d","resolution":{"observed_at":"2026-08-14T15:09:37.502485Z","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":"2026-08-14T15:09:37.507627Z","title":"Forty-third International Conference on Machine Learning Position Paper Track , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.507627Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:cdd7cf372cec28f61c14de7650c85dc0357696834de2e07f4a6cc4dfcff65d96","observation_id":"159d5cae-6112-4c7e-80cb-1885992394de","resolution":{"observed_at":"2026-08-14T15:09:37.507627Z","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":"2026-08-14T15:09:37.512166Z","title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.512166Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:40e9684b6d3afefc28972b2f6c0c0262ac33d9f762a74a69c23fbd11f9aab1ba","observation_id":"9bc2964c-cdc4-4096-b23b-05aaba63c149","resolution":{"observed_at":"2026-08-14T15:09:37.512166Z","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":"2026-08-14T15:09:37.516875Z","title":"The Fourteenth International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.516875Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:4344f64b9a0759fe4ddd2860d73fafcec19224fea480c576f99b3eb2e2daa168","observation_id":"b7717edc-cfae-4449-a264-77152cd664e6","resolution":{"observed_at":"2026-08-14T15:09:37.516875Z","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":"2026-08-14T15:09:37.521440Z","title":"Forty-second International Conference on Machine Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.521440Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:dd6bbd3800526079b0bcb45f3c97e21fb4c7bdf630cfdab10c7575471832690d","observation_id":"f32c866e-82bd-4eab-8d81-d4eea35cf69d","resolution":{"observed_at":"2026-08-14T15:09:37.521440Z","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":"2026-08-14T15:09:37.526010Z","title":"The Thirteenth International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.526010Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:0861d0d50151f534e5900ef407d647fe3e06a6ab60aba254b4c7bb9e1d4211db","observation_id":"ab655b5b-e71c-4838-afc1-95fe1fdd5657","resolution":{"observed_at":"2026-08-14T15:09:37.526010Z","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":"2026-08-14T15:09:37.530523Z","title":"The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.530523Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:ed2515556d26ae0631d2a3ccff5c1a59e0b9cc49fc442fa58e6af3d6d0544410","observation_id":"4937d64b-2752-46d1-b635-c71b5fdcad68","resolution":{"observed_at":"2026-08-14T15:09:37.530523Z","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":"2026-08-14T15:09:37.535234Z","title":"Gifford and Chandra Reddy and Jayant Kalagnanam , booktitle=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.535234Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:14de486a50bcea6a5cd3063f942dfc8b8807fdb04a81339d6b80cc563ef8468a","observation_id":"41c394c9-0802-4d9b-994f-b03ef37a9292","resolution":{"observed_at":"2026-08-14T15:09:37.535234Z","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":"2026-08-14T15:09:37.540037Z","title":"2026 , url=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.540037Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e2c5ebbfcea5e56bdc69c892deecfba1f58ab9606c1d730f6410ff6bb4413f9c","observation_id":"f233b6a8-ed00-492e-a1a8-d18b963a5fdd","resolution":{"observed_at":"2026-08-14T15:09:37.540037Z","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":"2026-08-14T15:09:37.544849Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.544849Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:2e85c7a98ebde31d0306bcbce0eabbdad5ba30fa72924835d3cefa147e95f7a4","observation_id":"b9e6bab2-1072-456b-95f7-adc5d84593f2","resolution":{"observed_at":"2026-08-14T15:09:37.544849Z","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":"2026-08-14T15:09:37.549742Z","title":"Proceedings of the 41st International Conference on Machine Learning , articleno =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.549742Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:d5e22ceeaeec53d16f62f5ad1f37570359f54077a3a1c670ecb91aa7b35d403a","observation_id":"a349b229-e797-4ce6-b4f9-81ed18b91a44","resolution":{"observed_at":"2026-08-14T15:09:37.549742Z","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":"2026-08-14T15:09:37.553908Z","title":"arXiv preprint arXiv:2602.06909 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.553908Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:3e2b27e2b3a6159fba555d2fbf942254fe2bfe2f6a963d1e98e4d0aba6b7cff2","observation_id":"6ee54ef8-3713-4a3d-96a0-4a3306cab57a","resolution":{"observed_at":"2026-08-14T15:09:37.553908Z","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":"2026-08-14T15:09:37.562099Z","title":"International Conference on Learning Representations , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.562099Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:6a0f4cf8ade42ce65935a0a7cd157cd8f0a47bb67d85ff23ac3eb751876be5bc","observation_id":"b6afb246-785e-463e-9bde-ed00c89c2dac","resolution":{"observed_at":"2026-08-14T15:09:37.562099Z","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":"2026-08-14T15:09:37.566922Z","title":"and Kaiser,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.566922Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:5651fc83656f9652359bc785defa9f464be899527839990483a9bdbf6af3ddc3","observation_id":"2be8d6b2-4322-46d2-98f1-5f43a9a77974","resolution":{"observed_at":"2026-08-14T15:09:37.566922Z","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":"2026-08-14T15:09:37.572352Z","title":"International Conference on Learning Representations (ICLR) , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.572352Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:c7fe167baa38210c404cef759b701aba62a3c94effbb530392e3197f5df2a45b","observation_id":"20b9dcc7-02a1-4b6a-b8f2-32e52b0fcb9c","resolution":{"observed_at":"2026-08-14T15:09:37.572352Z","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":"2026-08-14T15:09:37.577137Z","title":"International Conference on Learning Representations (ICLR) , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.577137Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:f9acb8e3c8d1d65be1a30d626e13f18109eef3e51bbfcde7bcc05c90671997ed","observation_id":"ecef3017-6fe8-4926-aebf-0ac0c518ca83","resolution":{"observed_at":"2026-08-14T15:09:37.577137Z","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":"2026-08-14T15:09:37.582185Z","title":"AAAI , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.582185Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:0efff2f31433698119e5a24a14073e77ab755b0e80343576376440192e1569a7","observation_id":"6cd46655-cd3c-4334-8039-e8faaa8d6057","resolution":{"observed_at":"2026-08-14T15:09:37.582185Z","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":"2026-08-14T15:09:37.587126Z","title":"International Conference on Learning Representations (ICLR) , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.587126Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:0b371c30ba4319a034eed8c1839110770f952a0d415387bbaa17a136eab4a701","observation_id":"1c1e0ae2-687f-4a68-8fb2-99c4ddf79814","resolution":{"observed_at":"2026-08-14T15:09:37.587126Z","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":"2026-08-14T15:09:37.591959Z","title":"International Journal of Forecasting , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.591959Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:69e1b8a087e05dee36685986e60827c9ff58ef7b14f4d4e0280edb6c54f712d1","observation_id":"1c9d8a0c-44f4-4a8b-a92d-8648045b388c","resolution":{"observed_at":"2026-08-14T15:09:37.591959Z","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":"2026-08-14T15:09:37.596874Z","title":"International Journal of Forecasting , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.596874Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:04a49182506ab6eabba2054045f189392395a846e5c0f61486340388088cccec","observation_id":"4811d356-7507-433e-9049-58e3939993f0","resolution":{"observed_at":"2026-08-14T15:09:37.596874Z","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":"2026-08-14T15:09:37.601530Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.601530Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:ae71c30fa1088d5f10d97dfb550efbc963b178009fbe2c025f276592dc25ac6d","observation_id":"42816d9b-f27e-4a64-a68c-ee61ee0f9554","resolution":{"observed_at":"2026-08-14T15:09:37.601530Z","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":"2026-08-14T15:09:37.606295Z","title":"Neurocomputing , volume=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.606295Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:fcf3eae8e4f9886836adde0b8f7e8d312966cb7b9e00e840b15283e70921ce0f","observation_id":"b51fd1f3-e9b6-4c84-bc14-9ba3c0d8dcbd","resolution":{"observed_at":"2026-08-14T15:09:37.606295Z","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":"2026-08-14T15:09:37.615782Z","title":"Long-term Forecasting with Ti","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.615782Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:cec69d48c151b1b78ec763257789b411191704d39e827f312eacd963d9fddfe1","observation_id":"f531b986-4191-4783-aa5d-6f320c300669","resolution":{"observed_at":"2026-08-14T15:09:37.615782Z","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":"2026-08-14T15:09:37.620089Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.620089Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:457da8cc6a5f8e7eb7f8306c82a4981ac1e4d1263a76bddc72137604871856ea","observation_id":"44e44ce8-bccc-4a30-ab0f-1493c7ebcf61","resolution":{"observed_at":"2026-08-14T15:09:37.620089Z","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":"2026-08-14T15:09:37.625458Z","title":"The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.625458Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:61dcc8d483b43a19c4f24ad867575577b508f3d1fc42607287e873a2ef2d67e2","observation_id":"415f1a22-4928-4b73-b579-784b4e7f8913","resolution":{"observed_at":"2026-08-14T15:09:37.625458Z","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":"2026-08-14T15:09:37.631360Z","title":"Journal of economic perspectives , volume=","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.631360Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:4509168e2f03739c499880f9498bd3fa33dffab64b05998cf6525d325dbcf319","observation_id":"1a05cfa3-c704-456f-a42e-43b717d9a45d","resolution":{"observed_at":"2026-08-14T15:09:37.631360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-14T15:09:37.641275Z","title":"arXiv preprint arXiv:2001.08361 , year=","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.641275Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:7bb21c3dfd400615be150f7b60a56aff7ec8535e1c436e49b5ee6d6eec9d0462","observation_id":"a6af82e7-9f6a-4e5b-8fef-a894bba0b94d","resolution":{"observed_at":"2026-08-14T15:09:37.641275Z","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":"2026-08-14T15:09:37.650578Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.650578Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:80a262c1addcebe08bdac943a3b21582a9cd3018a7fbcaf8df8ea6db59d224a7","observation_id":"7e393b3e-01a4-4d7f-9e44-10d2920443cc","resolution":{"observed_at":"2026-08-14T15:09:37.650578Z","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":"2026-08-14T15:09:37.661562Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.661562Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:011bfed184d6ac25631de7d5cba08e9c66723e33c0aeb32be802ec0f686be518","observation_id":"de0904ec-ec69-4c06-89a2-16beca83ec1c","resolution":{"observed_at":"2026-08-14T15:09:37.661562Z","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":"2026-08-14T15:09:37.671513Z","title":"2019 , publisher=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.671513Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:0584b1fc2f17cb11b9442c0702335d6e9669eba36238359d1663dbb7d92ea91f","observation_id":"14555251-6fbb-4803-8d25-3e2375859342","resolution":{"observed_at":"2026-08-14T15:09:37.671513Z","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":"2026-08-14T15:09:37.676355Z","title":"Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.676355Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:dc6bdb03341cf2dbf77c6ff89a505326c3f9f766b38aef4a28afc8d863c4a9c0","observation_id":"da1d35ee-7153-4dc5-a8e0-a5e8166597e3","resolution":{"observed_at":"2026-08-14T15:09:37.676355Z","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":"2026-08-14T15:09:37.681634Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.681634Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:939f103f5cb75b2a62a91ddd1a6c5bbf172a05699a3867f6dcc2b576221664c8","observation_id":"5823bb3b-f383-4c8a-9e38-362313f9ecb5","resolution":{"observed_at":"2026-08-14T15:09:37.681634Z","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":"2026-08-14T15:09:37.687191Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.687191Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:05e969dbfa48063fa41b10ed4a69b9c45becd64e97790050e5974dd5c81a84b2","observation_id":"33b5b847-43d8-4a41-9129-965cfee09a46","resolution":{"observed_at":"2026-08-14T15:09:37.687191Z","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":"2026-08-14T15:09:37.692845Z","title":"Neural Information Processing Systems , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.692845Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e9e4734b980735621170b1823c2872d7f70f81aaf1f28b775f582ada3fcadf0f","observation_id":"d985c301-0c91-4c63-a013-967795368adb","resolution":{"observed_at":"2026-08-14T15:09:37.692845Z","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":"2026-08-14T15:09:37.697998Z","title":"AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.697998Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:dc50eb9047a09e552abd029ea3b0ff2165e3fb51b3d0b7b7f949ebaaca170808","observation_id":"449ca322-46be-4660-ae77-4274e0a495ba","resolution":{"observed_at":"2026-08-14T15:09:37.697998Z","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":"2026-08-14T15:09:37.703080Z","title":"IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing , pages=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.703080Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:b1b0b8535286884fbf2c5aef8a97ac0b8beb99887b124714459b76d635756246","observation_id":"1f20a5bf-2773-4b50-b0ab-9dfc605b962d","resolution":{"observed_at":"2026-08-14T15:09:37.703080Z","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":"2026-08-14T15:09:37.707500Z","title":"2017 , note =","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.707500Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:cc76804a9b3cf493174b4ed4b10db9232139cd7303c0aebb183af8d4d99b89d6","observation_id":"3008b640-c2f5-42e0-b453-c6b07d153992","resolution":{"observed_at":"2026-08-14T15:09:37.707500Z","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":"2026-08-14T15:09:37.711424Z","title":"Expert Systems with Applications , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.711424Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:a7bd49559698503f5c976e68bf50958d481fb79e436bcc0b304fcd43a27df842","observation_id":"201042f5-a2ac-47be-ada4-3b3b7bf9fd3e","resolution":{"observed_at":"2026-08-14T15:09:37.711424Z","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":"2026-08-14T15:09:37.716737Z","title":"2015 , howpublished =","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.716737Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:a7445ec5a3b0dbeba92d93cb8409e8e130dfcd7cebb6bc02c81bf03022508842","observation_id":"8025ec94-7218-49f6-a90c-7fe4f026d477","resolution":{"observed_at":"2026-08-14T15:09:37.716737Z","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":"2026-08-14T15:09:37.726347Z","title":"The International ACM SIGIR Conference on Research & Development in Information Retrieval , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.726347Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:23b8166b06d789ee2f64806e12ead8bfa4ac77750d214b4cc59633099b92d889","observation_id":"63b72804-4186-46b3-93cb-990273ef98d8","resolution":{"observed_at":"2026-08-14T15:09:37.726347Z","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":"2026-08-14T15:09:37.730927Z","title":"The Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.730927Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:b172feee074a06e2e1c59d10c2df62a86243bd54460ff5adc4f14b234bd18259","observation_id":"99a37bdb-d211-4774-981d-4df803995ad6","resolution":{"observed_at":"2026-08-14T15:09:37.730927Z","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":"2026-08-14T15:09:37.741303Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.741303Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:21f857de6557aba70457d08a6ac925349f3d024d3b89602001e96c1218ceb1c3","observation_id":"80745e8f-37ac-4102-841a-4c79fc39c728","resolution":{"observed_at":"2026-08-14T15:09:37.741303Z","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":"2026-08-14T15:09:37.754839Z","title":"Forecasting using sparse cointegration , journal =","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.754839Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:5a45d5c6cccae1e395c4bf075503d783365a904d24295d8217404f557547669b","observation_id":"af192160-9a62-4e8e-a916-e462def81976","resolution":{"observed_at":"2026-08-14T15:09:37.754839Z","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":"2026-08-14T15:09:37.759060Z","title":"and Hyndman, Rob J","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.759060Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:2505342347a21f0cb835679d26a3078d5897937adf7d491bbbced8416bd405b7","observation_id":"d273fbec-3860-4473-a9ec-19d699dfb59e","resolution":{"observed_at":"2026-08-14T15:09:37.759060Z","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":"2026-08-14T15:09:37.776304Z","title":"2025 , note =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.776304Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:f376e01ffff17d21cc424d25b162c8c47106a3515a05fb9348fb38b01d998c82","observation_id":"3bc96f25-9c91-4003-8ba2-c02b683ebc18","resolution":{"observed_at":"2026-08-14T15:09:37.776304Z","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":"2026-08-14T15:09:37.781065Z","title":"2025 , note =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.781065Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:559eb65d0a2142c7075fdf1b90e6df6d81d6a36e1e6a7bc7a35f693c8fde0840","observation_id":"b786ebee-3004-4b1b-a133-6f69a4e33124","resolution":{"observed_at":"2026-08-14T15:09:37.781065Z","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":"2026-08-14T15:09:37.787179Z","title":"2025 , note =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.787179Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:33b94a82c7f1af0bb86b77d5d161b9fc554d70ea6d58b0ea6c4be1cee1c69989","observation_id":"ba8bc33e-b8c7-4773-a1cc-65ebca2f7cdf","resolution":{"observed_at":"2026-08-14T15:09:37.787179Z","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":"2026-08-14T15:09:37.792659Z","title":"2025 , note =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.792659Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:5eb7bd2a6cdb0bfc14e4b655608847a9bc032dc11ed33ccd95f0e61608a6bfc3","observation_id":"dfac7987-c0bc-43fa-9a01-7629b9503123","resolution":{"observed_at":"2026-08-14T15:09:37.792659Z","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":"2026-08-14T15:09:37.797153Z","title":"2020 , note =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.797153Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:0bb978eadbd451e99baec56144651c3d62da9dbcfbcb9927bb955f6a667cc352","observation_id":"4b43416d-288d-4e7a-b9df-c04dfbdc618c","resolution":{"observed_at":"2026-08-14T15:09:37.797153Z","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":"2026-08-14T15:09:37.801163Z","title":"2015 , howpublished =","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.801163Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:26fc744281297a16e58a2cfd855a458e4873a3d4bf659923085c5232cda9b537","observation_id":"cf62aadb-41fc-4fd0-a150-ae37fa515d44","resolution":{"observed_at":"2026-08-14T15:09:37.801163Z","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":"2026-08-14T15:09:37.805505Z","title":"2014 , howpublished =","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.805505Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:5ac5624b37778fe2f3370b3c540e540461b0064d37bea7a1c3ebb873cee399e6","observation_id":"50c8dab7-a894-49aa-84cc-208160ef754f","resolution":{"observed_at":"2026-08-14T15:09:37.805505Z","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":"2026-08-14T15:09:37.810033Z","title":"2024 , howpublished =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.810033Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:16acae926e7813e48d4643b13bb0df15f460a2226f1701a1db5bb104f18aede0","observation_id":"188d995c-b5fb-4a82-bf81-d52fca21ac59","resolution":{"observed_at":"2026-08-14T15:09:37.810033Z","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":"2026-08-14T15:09:37.815511Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.815511Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:c682b73618b22f32154eff8976a9e7b4ce06ffd3450158f4cd9a59a3196a5c59","observation_id":"181c3c5f-64d5-47a8-8100-2be1b1715e13","resolution":{"observed_at":"2026-08-14T15:09:37.815511Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-14T15:09:37.820293Z","title":"Christiano and Martin Eichenbaum and Charles L","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.820293Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:2c9a9e76affe4647c8fddd8e758fa4680147870261da3fce263d51b54ee43633","observation_id":"c406debb-60b0-437c-b772-58752f083fab","resolution":{"observed_at":"2026-08-14T15:09:37.820293Z","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":"2026-08-14T15:09:37.825545Z","title":"Communications of the ACM , volume=","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.825545Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:3388f239bee31a49e7bbf5021508ef5bd66fbd4c9cd08fe91b64be885d53f075","observation_id":"856cf1c8-d420-4f6e-a940-fcd3208a7396","resolution":{"observed_at":"2026-08-14T15:09:37.825545Z","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":"2026-08-14T15:09:37.831281Z","title":"Transactions on Machine Learning Research , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.831281Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:d6e22333706d22535e179cf422ece07bbc75cc56128cbaf4546312b5428d11df","observation_id":"b7538abf-8ee9-4edb-b4e0-ef21360a2c1e","resolution":{"observed_at":"2026-08-14T15:09:37.831281Z","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":"2026-08-14T15:09:37.837735Z","title":"Version 2020-10-06 , author=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.837735Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:1612ac5705a5c713c0c95ff99500248d440e8dceaf9c96f0c7843d466146632f","observation_id":"96a7a00a-aeac-45a4-baf1-8c81964c50c0","resolution":{"observed_at":"2026-08-14T15:09:37.837735Z","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":"2026-08-14T15:09:37.843286Z","title":"Nature Energy , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.843286Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:9dea6e0bc2dc04e42c4c05c7123ede42c614f962a077078b53aaaba0b9554ddd","observation_id":"c4b28163-7bff-40d2-b56a-529df2ecf293","resolution":{"observed_at":"2026-08-14T15:09:37.843286Z","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":"2026-08-14T15:09:37.847962Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.847962Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:2790adb5715b796deeafbbb10dd3bd8a160ab17fdc4aa311b3ac87eabef4ca56","observation_id":"bbee30f2-fba8-4f8a-a21d-ea453a0cd069","resolution":{"observed_at":"2026-08-14T15:09:37.847962Z","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":"2026-08-14T15:09:37.868114Z","title":"International Journal of Forecasting , volume=","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.868114Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:ce318fa77ed09fd21ae3bc59adc86c7d4a9cb9e6b09c57cc2fd1f53b7160b4d7","observation_id":"a63f8d33-e0c4-4ffa-9479-d7bf763d1a2f","resolution":{"observed_at":"2026-08-14T15:09:37.868114Z","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":"2026-08-14T15:09:37.873856Z","title":"2025 , note=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.873856Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:beaca8b521e917b9009a28fca00e4d9b3654cf36608abdb3745fadd9a926bf4f","observation_id":"ee04c8ca-3d9c-4e02-8b62-b488cfe76e40","resolution":{"observed_at":"2026-08-14T15:09:37.873856Z","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":"2026-08-14T15:09:37.879362Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.879362Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:af96c8b465baa3ba1203394ae73b8e46550ff6dd1de5be5544417da3b12f5a58","observation_id":"29ea5ca2-8ea3-4a03-af1e-f73ff9f7d413","resolution":{"observed_at":"2026-08-14T15:09:37.879362Z","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":"2026-08-14T15:09:37.898524Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.898524Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e04e844313618933f10877124d1e29a487984bef25c5a8c328d3f3fc77c2aa06","observation_id":"938c673d-c6f1-4695-996f-f20ce606e039","resolution":{"observed_at":"2026-08-14T15:09:37.898524Z","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":"2026-08-14T15:09:37.902481Z","title":"Walmart recruiting - store sales forecasting","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.902481Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:d8182c44663f0313066eae8de0898c3197e10621a5c4e5d32ddca6f8a3d21968","observation_id":"89383790-c652-4fc7-8989-562e44bb3a20","resolution":{"observed_at":"2026-08-14T15:09:37.902481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10393","last_updated":"2024-11-11T04:48:24Z","snapshot_observed_at":"2026-08-18T08:07:26.052265Z","submitted_at":"2024-10-14T11:29:38Z","title":"GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10393","snapshot_observed_at":"2026-08-14T15:09:37.911640Z","title":"Gift-eval: A benchmark for general time series forecasting model evaluation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.911640Z"},"links":{"cited_paper":"/paper/2410.10393","citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:dcd6dd1b010d60994d8ea58a831f3b052ad2a9da6807b65c0abe57d13d7f2b7b","observation_id":"9b405689-3c15-4052-b1d5-4f83b2626e9f","resolution":{"observed_at":"2026-08-14T15:09:37.911640Z","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":"2026-08-14T15:09:37.916594Z","title":"Chronos: Learning the language of time series","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.916594Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:30a1368f289968eb1a8e80f253d1db7f0b3a207e45bd4ed63d70af4a65ecc605","observation_id":"edd2413f-ba97-4958-8776-77a04e1b7181","resolution":{"observed_at":"2026-08-14T15:09:37.916594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.15821","last_updated":"2025-10-17T17:00:53Z","snapshot_observed_at":"2026-08-17T22:08:09.605037Z","submitted_at":"2025-10-17T17:00:53Z","title":"Chronos-2: From Univariate to Universal Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.15821","snapshot_observed_at":"2026-08-14T15:09:37.921531Z","title":"Chronos-2: From univariate to universal forecasting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.921531Z"},"links":{"cited_paper":"/paper/2510.15821","citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:74eaa7842e9fe9908e03c9b37a0207684b290e723ced43e319be2f62dd71d5f8","observation_id":"cb60a268-7b4a-40a4-b022-f2c42b4e5fd1","resolution":{"observed_at":"2026-08-14T15:09:37.921531Z","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":"2026-08-14T15:09:37.926429Z","title":"o ck, G \\","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.926429Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:85c57db6ec1db87903bb1ba9c3066677330fa7235810f09bb50542f2c2fee9b4","observation_id":"beb4a168-5bd7-45b0-8d9b-6e3e77bccc02","resolution":{"observed_at":"2026-08-14T15:09:37.926429Z","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":"2026-08-14T15:09:37.931707Z","title":"Recurrent neural networks for multivariate time series with missing values","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":109,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.931707Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:7e50224d4896e7c4307452b65a805e61d4e15cea8ff35a1e3b7ea62a3d325974","observation_id":"17209f10-8779-46a8-9b32-c3868182b62b","resolution":{"observed_at":"2026-08-14T15:09:37.931707Z","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":"2026-08-14T15:09:37.936847Z","title":"Time is not enough: Time-frequency based explanation for time-series black-box models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.936847Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e8d1436a73f030fcb689036d7cf32df19c2c5686cbe179adc6bd472fac437866","observation_id":"d8cce76c-1b79-4ad6-9824-b1004675a37c","resolution":{"observed_at":"2026-08-14T15:09:37.936847Z","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":"2026-08-14T15:09:37.942041Z","title":"This time is different: An observability perspective on time series foundation models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":111,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.942041Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:834954a8f664d50e038a64168e93caf3c3337daace7912651c38997953540298","observation_id":"dbc579d8-0947-4730-a47e-6218b0abc6a4","resolution":{"observed_at":"2026-08-14T15:09:37.942041Z","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":"2026-08-14T15:09:37.946464Z","title":"A decoder-only foundation model for time-series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":112,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.946464Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:e539be4d9cee3fb77d014f1dbd1231019c07b18351082a09995d7c74efcc4bac","observation_id":"d8fb4065-7861-4a90-84ec-094de54274cf","resolution":{"observed_at":"2026-08-14T15:09:37.946464Z","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":"2026-08-14T15:09:37.950909Z","title":"Data package time series","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":113,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.950909Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:a9b084ce9729ed1ffbcdf8e30addd9921d2af545e4d005756b1890c0bef7d694","observation_id":"eae137d2-5b26-471d-bd55-46b6f8b1128a","resolution":{"observed_at":"2026-08-14T15:09:37.950909Z","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":"2026-08-14T15:09:40.192342Z","title":"UK COVID-19 dashboard data","venue":null,"work_id":"a40667f2-ae8c-42ee-91a9-c86455abd36e","year":2022},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":114,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.955947Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:a8742201e8dc41a1a1205d44931f832625ef2d4f7127dc1e0bf48daf41e8679f","observation_id":"8aca77fc-6c01-4005-a310-9802ec473102","resolution":{"observed_at":"2026-08-14T15:09:40.198617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.03224","last_updated":"2023-09-11T09:06:28Z","snapshot_observed_at":"2026-08-17T08:27:22.578323Z","submitted_at":"2022-02-07T14:24:44Z","title":"HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.03224","snapshot_observed_at":"2026-08-14T15:09:37.959837Z","title":"H ERMES : Hybrid error-corrector model with inclusion of external signals for nonstationary fashion time series","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":115,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.959837Z"},"links":{"cited_paper":"/paper/2202.03224","citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:929546af17a672c84ed0a4aba475d234532c42bbc1a1247e4d38ee01ed6961bc","observation_id":"3b0e1129-d682-4cb3-8965-a5d067e1d678","resolution":{"observed_at":"2026-08-14T15:09:37.959837Z","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":"2026-08-14T15:09:37.963805Z","title":"De Vito , E","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":116,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.963805Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:4e30385c542057624e839c36419d73df19cc05a0c0544789fe26b44e472e4e93","observation_id":"bb2f9af6-b32a-4ddb-b64e-5b013341bd2c","resolution":{"observed_at":"2026-08-14T15:09:37.963805Z","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":"2026-08-14T15:09:40.176231Z","title":"Respiratory viruses weekly data","venue":null,"work_id":"01b85a39-33a2-4b70-a5ab-ae8f24193c4a","year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":117,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.967672Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:9d3313fbd15ef1ef54539f22b78117a35ea6306eaf2d81b57be6a97b7c2676c1","observation_id":"5c52e48a-4912-4e09-a2c4-d8359c59b9c8","resolution":{"observed_at":"2026-08-14T15:09:40.181549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.158995Z","title":"Gifford, Chandra Reddy, and Jayant Kalagnanam","venue":null,"work_id":"768527cd-59d4-4e53-92b3-6a0094cac429","year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":118,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.971719Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:dd85925df5023fa0be41cd3db04370226c44211fcdef3b797b6fe080c344a558","observation_id":"eee633be-79ea-425f-bbf6-f3feb3ddd51a","resolution":{"observed_at":"2026-08-14T15:09:40.164631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:37.975771Z","title":"How not to lie with statistics: the correct way to summarize benchmark results","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":119,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.975771Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:41af9aff22136680e07e622cfa286cf88d025224525d5ece93b1d0f48f0c9fce","observation_id":"8783e033-f77b-4baa-9159-eb221f826896","resolution":{"observed_at":"2026-08-14T15:09:37.975771Z","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":"2026-08-14T15:09:40.133108Z","title":"Rossmann store sales","venue":null,"work_id":"de8a8951-eec2-4854-af88-166f373ddcce","year":2015},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.980387Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:435ef8420388e163d4c5eebeb43798a67ac656e4570dc6318b1613def869b346","observation_id":"9ecaac6c-d550-4eec-877c-7a6e5740ede8","resolution":{"observed_at":"2026-08-14T15:09:40.138379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.116760Z","title":"Webb, Rob Hyndman, and Pablo Montero-Manso","venue":null,"work_id":"2f5290f5-3a84-4053-8b49-8fb41a4320b6","year":2021},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":121,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.985726Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:57134d3a514ae75fe1adc875837a476de633f6b32f3b57f5ed46679268f1a46d","observation_id":"64c8c2f2-7fa3-4ac6-bffc-2ddba1b2aa58","resolution":{"observed_at":"2026-08-14T15:09:40.121862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.100291Z","title":"Moment: a family of open time-series foundation models","venue":null,"work_id":"711b5dea-cb6b-4da9-9daf-b31c02d92427","year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":122,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.990516Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:1322071fe1b5b231800abc061dd4fc015c76f6c1c53ec2580de2d1a7ce9b0580","observation_id":"85c21c07-b95a-4837-a772-0c1a766a0906","resolution":{"observed_at":"2026-08-14T15:09:40.105262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.083878Z","title":"M OMENT : A family of open time-series foundation models","venue":null,"work_id":"e5d3a2f7-ce3b-4a15-bb32-11c29f2a6e1f","year":2024},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":123,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.994904Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:27de9cf59e4b5d5308c0f696ef6b4e8698d8c76769e087840610b2bd365dee16","observation_id":"7896f630-03be-45b4-8c59-ce0a278123d7","resolution":{"observed_at":"2026-08-14T15:09:40.089747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.13986","last_updated":"2026-05-28T17:22:15Z","snapshot_observed_at":"2026-08-13T12:18:44.475388Z","submitted_at":"2026-05-13T18:01:43Z","title":"TabPFN-3: Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.13986","snapshot_observed_at":"2026-08-14T15:09:37.999336Z","title":"o ge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Mihir Manium, Shi Bin Hoo, Magnus B \\","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":124,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:37.999336Z"},"links":{"cited_paper":"/paper/2605.13986","citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:addf3ef2befa4ae18302faed9e6dce65d27631ddf9fb594818b43594d46eb1dc","observation_id":"a4e43f3b-8a90-469a-82c2-1210a5a82823","resolution":{"observed_at":"2026-08-14T15:09:37.999336Z","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":"2026-08-14T15:09:40.067661Z","title":"GTM : A general time-series model for enhanced representation learning of time-series data","venue":null,"work_id":"586809b9-3eca-4195-b11a-8e402ced3110","year":2026},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":125,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:38.004541Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:414d9bcab9e12b174db938c21f295b3cb7473e54dffdd8023424cebc5224dcc1","observation_id":"05994195-2805-4b29-a6a6-c59c188bbe09","resolution":{"observed_at":"2026-08-14T15:09:40.073414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.050977Z","title":"Global energy forecasting competition 2012","venue":null,"work_id":"3c1e514c-0711-4ddb-b452-eff43c9164fa","year":2012},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":126,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:38.009923Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:07e2fb1f39c1190783e1eb257cf6a9216ebd77a6e04767a8c43397ad89b02a9e","observation_id":"82f0df75-8bf2-44ef-93b3-4d932ba4f87d","resolution":{"observed_at":"2026-08-14T15:09:40.056787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:38.014558Z","title":"From tables to time: Extending tabpfn-v2 to time series forecasting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":127,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:38.014558Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:72369381b29e0220077c1b053102a1e3d5c649e66ac71eedf53c2d880c1978af","observation_id":"8db9976e-7691-45f3-8f9e-fed1b684a59f","resolution":{"observed_at":"2026-08-14T15:09:38.014558Z","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":"2026-08-14T15:09:40.033927Z","title":"Recruit restaurant visitor forecasting","venue":null,"work_id":"8036de27-0ef7-45b4-af43-fde31f1015b7","year":2017},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":128,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:38.019233Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:ac8e7d41d08d2cc72026a0bb63fd2238972cafb1d9b248df22dfcdc6ee05cae6","observation_id":"9ab5541e-3dfc-492f-9fa4-1f596b0ea66e","resolution":{"observed_at":"2026-08-14T15:09:40.039295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:40.017188Z","title":"The landscape of agentic time series systems: Architectures, reliability, and frontiers","venue":null,"work_id":"4aac9cbe-db3c-4e4b-a314-595136d9f715","year":null},"citing_paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","version":1},"reference_index":129,"source":"arxiv_source","source_observed_at":"2026-08-14T15:09:38.023988Z"},"links":{"citing_paper":"/paper/2608.13262"},"observation_digest":"sha256:323d139190fd1b2685ebd087de16cd75e77d12d13c60f79b06bb548a3bf3d6db","observation_id":"8eaed97c-797f-4325-a7d1-7d107dc98865","resolution":{"observed_at":"2026-08-14T15:09:40.023120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.13262","last_updated":"2026-08-13T14:00:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T20:11:17.105246Z","submitted_at":"2026-08-13T14:00:39Z","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":88,"verified_exact":0,"verified_fuzzy":11},"total_outbound_references":155},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 100 of 155 outbound references and 0 inbound Pith citation observations for arXiv:2608.13262."}