{"as_of":"2026-08-07T09:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d449db6a357f76a16e4672f5055ea23ba8227ec2353e3531e244864b1c9c6798","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:35:27.939208Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.13043/citation-record","integrity":"/paper/2507.13043/integrity","json":"/paper/2507.13043/citation-record.json","paper":"/paper/2507.13043"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:35:35.523193Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":"37661ae6-8090-4145-88b2-9449387377af","year":2021},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.200565Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:0821f1f917f55ba5a8762d4c912ca96c135960e1bfc61c9b222755b71827f732","observation_id":"285fd53e-e300-4d76-8b71-bf1412e201a9","resolution":{"observed_at":"2026-08-06T16:35:35.672808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:35.217861Z","title":"Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecasting,","venue":null,"work_id":"7ce33b16-b0a4-4d46-8082-f40a60d13512","year":2021},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.243125Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:4a519cb12d10b0d2381e371ee5695297f3e2d1b440b818fbb98d73af4ecb08c8","observation_id":"04fc4dbe-5689-481e-90c6-7b7e80b91ed9","resolution":{"observed_at":"2026-08-06T16:35:35.366098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:35.030378Z","title":"Fedformer: Frequency en- hanced decomposed transformer for long-term series forecasting,","venue":null,"work_id":"d4929049-16d1-4b70-902f-7fa82a6c9c8a","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.317622Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:2daddb9a8c4f1c126c8f1992c5946e321dce01a624cf28520c32598dac1b273d","observation_id":"795f50c6-46d3-4596-9215-1d586d6c04be","resolution":{"observed_at":"2026-08-06T16:35:35.100923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:34.906906Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":"8bd1e310-fe50-4d06-893b-3bcf8aedc50a","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.377341Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:9b499763572a3ec7d46f0497e709363e927d300bbc50b45c00703d2706c3051a","observation_id":"5c4b6c06-bda2-45f4-b608-322ca418c43b","resolution":{"observed_at":"2026-08-06T16:35:34.963885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06625","last_updated":"2024-03-14T11:45:57Z","snapshot_observed_at":"2026-07-06T16:30:29.783501Z","submitted_at":"2023-10-10T13:44:09Z","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06625","snapshot_observed_at":"2026-08-06T16:35:23.497222Z","title":"itransformer: Inverted transformers are effective for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.497222Z"},"links":{"cited_paper":"/paper/2310.06625","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:e0eed8f7bab52c6372b96474adc29469a8df4e9a365ac86cc692541a8b0b9229","observation_id":"24470776-ac26-4567-b135-421bcb41802b","resolution":{"observed_at":"2026-08-06T16:35:23.497222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19072","last_updated":"2024-11-11T03:18:32Z","snapshot_observed_at":"2026-08-04T15:44:46.544371Z","submitted_at":"2024-02-29T11:54:35Z","title":"TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19072","snapshot_observed_at":"2026-08-06T16:35:23.638856Z","title":"Timexer: Empowering transformers for time series forecasting with exogenous variables,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.638856Z"},"links":{"cited_paper":"/paper/2402.19072","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:a5d5f25078d3555a6546ea6e1a4533644d525c73675b772258afdeeb489b44d3","observation_id":"13bcf5f8-85c5-4230-b493-b88f96aa7612","resolution":{"observed_at":"2026-08-06T16:35:23.638856Z","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":"2026-08-06T16:35:23.708925Z","title":"Autoregressive moving-average attention mechanism for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.708925Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:d5ad45062b53588e71e1c0bc7db3e039dfb183ae060c3add5b27eacec84d1e12","observation_id":"1e11aba9-2c0a-4b1f-9ddd-8ff12491f50f","resolution":{"observed_at":"2026-08-06T16:35:23.708925Z","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":"2026-08-06T16:35:34.799098Z","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,","venue":null,"work_id":"d0b4be90-5a9f-4c3e-93ef-6da4ccde1a34","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.787211Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:4c8b8f5605a85b21494bbd0215cfba78740b46c16fb10568116ee15805e10f50","observation_id":"2fd6bcf1-25f3-4a91-a876-c66d17c4d107","resolution":{"observed_at":"2026-08-06T16:35:34.847325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:34.687482Z","title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting,","venue":null,"work_id":"09cf9a94-69b4-4d3a-829a-e444feecbf65","year":2021},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.851697Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:799771d2794b8059ac8b69831fec0cce53a849e3625af41027855a5b7b86fce2","observation_id":"e5e6122b-fcd9-465b-ad95-91a02a16c946","resolution":{"observed_at":"2026-08-06T16:35:34.746394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:34.535432Z","title":"Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,","venue":null,"work_id":"ed611968-bfe0-4543-8340-7c8512652d37","year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:23.950284Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:463a6b06991c62c684478b1ab75e760ccdaf80e476f830d8cb5b76f761baac4c","observation_id":"219144e4-c5a2-4084-b8e8-6c1044139845","resolution":{"observed_at":"2026-08-06T16:35:34.598058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:34.403192Z","title":"Basisformer: Attention-based time series forecasting with learnable and interpretable basis,","venue":null,"work_id":"83e772af-43da-40fd-a42e-e7b83f168f47","year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.019230Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:f52cdb7b5200f72d627c0b2f13db1f346dd9d6f88a374d70158e1a0f97d556d8","observation_id":"870cd516-df43-44fd-bbcc-c41f27ea2d51","resolution":{"observed_at":"2026-08-06T16:35:34.463667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:34.150668Z","title":"Samformer: Unlocking the potential of transformers in time series forecasting with sharpness-aware minimization and channel-wise attention,","venue":null,"work_id":"8635c3b1-78b9-41c1-a24e-da2ad79c1228","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.134470Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:ad2f75c7eff3493e8adf02c44f6b8516ddc135e021f31d0287b6c7809dfd1889","observation_id":"8b070c2f-d220-4d23-8860-ff5134be04f2","resolution":{"observed_at":"2026-08-06T16:35:34.238975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04038","last_updated":"2023-02-07T00:30:26Z","snapshot_observed_at":"2026-07-06T13:18:44.773914Z","submitted_at":"2022-06-08T17:54:26Z","title":"Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04038","snapshot_observed_at":"2026-08-06T16:35:24.213037Z","title":"Scaleformer: Iterative multi-scale refining transformers for time series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.213037Z"},"links":{"cited_paper":"/paper/2206.04038","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:843e8dec5f53d019a4ea6bdadafa9db2bf7a996354d9d3332fed921706f69070","observation_id":"6084ccce-90ee-4ff1-a934-6e3469674a82","resolution":{"observed_at":"2026-08-06T16:35:24.213037Z","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":"2026-08-06T16:35:33.896640Z","title":"Learning to rotate: Quaternion transformer for complicated periodical time series forecasting,","venue":null,"work_id":"dccc9f92-ab20-4004-9b27-ea412f3543f8","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.278473Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:eb495254f1433757a6d159c3b5dd1e8bd98ff9b896dd3e4e701b9288f44b222c","observation_id":"e3b8d7e1-9b72-4c59-83e8-02688f7a7647","resolution":{"observed_at":"2026-08-06T16:35:34.017976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:33.583276Z","title":"Forecasting natural gas consumption in istanbul using neural networks and multivariate time series methods,","venue":null,"work_id":"e82c50a1-b151-45d5-9647-8d91ff9d7417","year":2012},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.365613Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:3512e71b087d7c69a67280d3d635665f88bd1207d8715099bf1a61809c11e5fa","observation_id":"086d1bca-f0ec-4516-8a69-169c4e91ddc4","resolution":{"observed_at":"2026-08-06T16:35:33.763620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:33.331703Z","title":"A review on time series forecasting techniques for building energy consumption,","venue":null,"work_id":"11f2e872-abd4-4328-bf9e-b54cf0acd6f9","year":2017},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.423076Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:e2039d6543d9323be44a11df7df612d72f374326677b0a54355075b9f0971fff","observation_id":"a3fda1d4-36e5-49bc-9293-e8c4498e17ba","resolution":{"observed_at":"2026-08-06T16:35:33.459034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:33.195475Z","title":"Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches,","venue":null,"work_id":"f7d87a10-1b97-46fc-8cd5-7374f97d5615","year":2019},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.481939Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:41588b63a17450882cbc71f50593b44000c862ee896f8322ff6ef6e263c49e38","observation_id":"0dac1727-2346-4c62-a41b-076aaf714017","resolution":{"observed_at":"2026-08-06T16:35:33.271698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:33.076707Z","title":"Web service recommendation based on time series forecasting and collaborative filtering,","venue":null,"work_id":"6d6cdb8a-a9d9-4ba9-abab-f6d622d2d057","year":2015},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.561064Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:832ef22a1cb209562a8f2133aaf9b576d98754282d1eccadd6236b7f05b3c7c7","observation_id":"cd7f9ef7-799a-4ebc-8d7a-5d33f4d58c31","resolution":{"observed_at":"2026-08-06T16:35:33.123804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:32.881953Z","title":"An econometric time series forecasting framework for web services recommendation,","venue":null,"work_id":"2dfe41b1-393f-4b92-ae5d-bc189c355bb5","year":2020},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.628547Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:2d3304f54fa3978be53652535cd9ccc90adbc05e9171e0d290fb3ddea8587fc4","observation_id":"11fd23fe-b3ca-4493-a2a4-473d7982d128","resolution":{"observed_at":"2026-08-06T16:35:32.981861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:32.663456Z","title":"Neural net time series forecasting framework for time-aware web services recommendation,","venue":null,"work_id":"6b50861b-6aa6-4aa5-b338-dca6ad4ab07b","year":2020},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.681227Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:2d7b980124ddf248dd2a2180169de6a0160e43cd47a1a384cdf24fa93773e407","observation_id":"a7fff938-15e8-419f-8046-f430472fa65c","resolution":{"observed_at":"2026-08-06T16:35:32.767493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:32.463821Z","title":"Multivariate time series dataset for space weather data analytics,","venue":null,"work_id":"7c18b08e-f1a7-470e-971d-d1eac0bd2be6","year":2020},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.748810Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:283829edcb8e50a6975ab7e69eb159cbfbad468646a6fd5201b865aac4d4237f","observation_id":"7ee5e3cb-1f35-48fc-92d5-9898efc8ae66","resolution":{"observed_at":"2026-08-06T16:35:32.567127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:32.251316Z","title":"Transductive lstm for time-series prediction: An application to weather forecasting,","venue":null,"work_id":"d04e4d3c-c436-43e0-916d-4894f69086cb","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.813785Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:fc40d4b52ccd981271040ebbb57a8d01f46d8f9f412a87c0aeab0877b3af4e15","observation_id":"79a89079-7b5b-44c8-88b5-0b66f00c0b5e","resolution":{"observed_at":"2026-08-06T16:35:32.343279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:31.805950Z","title":"Chapter 16 - copula methods for forecasting multivariate time series,","venue":null,"work_id":"eb2ccebe-550b-4809-b829-95f0a81e83dd","year":2013},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.925924Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:46a1921c8ebdf692f8b9cf7e7129ae5bbe90339180b8273acfa6f7933b0973b5","observation_id":"727adb08-d751-4674-beae-56eb0b633c65","resolution":{"observed_at":"2026-08-06T16:35:31.930169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:31.596878Z","title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,","venue":null,"work_id":"a900867e-12ea-4c7f-a1c0-583f57b68a9a","year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.160042Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:26723a9245937e2b246d2fb1068142d32bdea30ae3948698cf670945140ecb1d","observation_id":"e1f7dab6-007e-4857-84cc-68a1c5b110f7","resolution":{"observed_at":"2026-08-06T16:35:31.694540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:31.362520Z","title":"Fredformer: Fre- quency debiased transformer for time series forecasting,","venue":null,"work_id":"ab38f297-db5d-4260-a305-fb2f66325ab4","year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.224754Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:a2967a5046be7ffce5ab6290a32705b5c509d70ae877cc0028945cbb66f501d6","observation_id":"d9308b56-b5ba-43aa-a7a1-e42cc11fea82","resolution":{"observed_at":"2026-08-06T16:35:31.455796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05956","last_updated":"2024-09-15T04:57:36Z","snapshot_observed_at":"2026-08-02T17:01:30.855704Z","submitted_at":"2024-02-04T15:33:58Z","title":"Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05956","snapshot_observed_at":"2026-08-06T16:35:25.298249Z","title":"Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.298249Z"},"links":{"cited_paper":"/paper/2402.05956","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:d7b67f55cbca0463ec85c97a0ab2347965653d7db6b65ca458f659258567929f","observation_id":"a9b10124-3a0c-46e2-968a-74e06bc6fc36","resolution":{"observed_at":"2026-08-06T16:35:25.298249Z","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":"2026-08-06T16:35:31.117417Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer,","venue":null,"work_id":"937d172c-0435-481d-87de-d744f92e1de1","year":2020},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.384758Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:69aa041a85b962432ad5083e8af04208d22156c6bf99d5104cb4eb80757fa2d2","observation_id":"27bdeb32-d01a-462a-abe9-9a63a24971ff","resolution":{"observed_at":"2026-08-06T16:35:31.261382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:30.863930Z","title":"What language model architecture and pretraining objective works best for zero-shot generalization?","venue":null,"work_id":"2c66eeaa-30ee-45c1-b392-568f8af01037","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.472244Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:88d6c2ae6a1dbe8cf9ba3eb9f0886d92dc692968b877440e9516ce41ded97955","observation_id":"a7ce6d2b-c540-43e5-a8e4-d6c6fe3f1495","resolution":{"observed_at":"2026-08-06T16:35:30.954760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.04052","last_updated":"2023-04-08T15:44:29Z","snapshot_observed_at":"2026-07-06T15:13:36.835356Z","submitted_at":"2023-04-08T15:44:29Z","title":"Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.04052","snapshot_observed_at":"2026-08-06T16:35:25.549217Z","title":"Decoder-only or encoder-decoder? interpreting language model as a regularized encoder-decoder,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.549217Z"},"links":{"cited_paper":"/paper/2304.04052","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:34dcef4487f3221426073df287802cea2c1a9dcdb468535f3264f3f3edfb9e77","observation_id":"8e675d1c-7d07-40a0-b69e-4270be5d16c1","resolution":{"observed_at":"2026-08-06T16:35:25.549217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12351","last_updated":"2024-02-23T19:22:58Z","snapshot_observed_at":"2026-07-06T16:50:22.657866Z","submitted_at":"2023-11-21T04:59:17Z","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12351","snapshot_observed_at":"2026-08-06T16:35:25.620979Z","title":"Advancing transformer architecture in long-context large language models: A comprehensive survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.620979Z"},"links":{"cited_paper":"/paper/2311.12351","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:05de1ae431daea9badda2dd87a002b2bd210ef88b62e695936b688732693581e","observation_id":"a023b1db-0d76-405f-80f2-b3ee1ceb058c","resolution":{"observed_at":"2026-08-06T16:35:25.620979Z","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":"2026-08-06T16:35:30.672563Z","title":"A review on large language models: Architectures, applications, taxonomies, open issues and challenges,","venue":null,"work_id":"d8e26ad8-ae12-4c5d-8724-4ed71785c5b0","year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.666371Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:c960e338af394e1b9a355a8a6d63adcea8df2f07a52fc8117cf2912b427aae52","observation_id":"b6962571-700b-4a53-a1b9-b241f2c13f56","resolution":{"observed_at":"2026-08-06T16:35:30.763084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:25.723089Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.723089Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:0993df64c45136b76f0b5ca3649ed4042086cc8a81f21d3fa85d9722e366e2c4","observation_id":"71ad005d-879a-4429-b6c6-1f44158d6fe5","resolution":{"observed_at":"2026-08-06T16:35:25.723089Z","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":"2026-08-06T16:35:30.462813Z","title":"A transformer-based framework for multivariate time series representation learn- ing,","venue":null,"work_id":"94654c05-3325-4381-a8f2-499b12df4ce4","year":2021},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.827554Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:b56f47512e882c297e2e1795d7f5cf32cf3a35a65cd476e3b1c0587748b0dc44","observation_id":"a4cb961e-fc61-4216-871e-fd15000ca2d1","resolution":{"observed_at":"2026-08-06T16:35:30.525875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08472","last_updated":"2024-05-06T04:00:17Z","snapshot_observed_at":"2026-08-06T22:05:52.494138Z","submitted_at":"2024-04-12T13:41:29Z","title":"TSLANet: Rethinking Transformers for Time Series Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08472","snapshot_observed_at":"2026-08-06T16:35:25.899010Z","title":"Tslanet: Rethinking transformers for time series representation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.899010Z"},"links":{"cited_paper":"/paper/2404.08472","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:2097ddbcd2e1acf0363ae3ae39932c6253b826abf95377c70683c713d4b1e1d3","observation_id":"9f08df83-03ca-458e-80a3-d3d6aa5b95bd","resolution":{"observed_at":"2026-08-06T16:35:25.899010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16877","last_updated":"2024-12-23T13:34:55Z","snapshot_observed_at":"2026-07-06T18:20:22.759333Z","submitted_at":"2024-05-27T06:49:39Z","title":"Are Self-Attentions Effective for Time Series Forecasting?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16877","snapshot_observed_at":"2026-08-06T16:35:26.015484Z","title":"Are self-attentions effective for time series forecasting?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.015484Z"},"links":{"cited_paper":"/paper/2405.16877","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:6ce1ec7add8010d640446437a2e6dfd1cc183942022142f1f7c6452593f04ad4","observation_id":"60533e89-7bd0-4c05-91e5-fd838c42d6e0","resolution":{"observed_at":"2026-08-06T16:35:26.015484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07125","last_updated":"2023-05-11T21:47:52Z","snapshot_observed_at":"2026-08-06T09:46:00.800448Z","submitted_at":"2022-02-15T01:43:27Z","title":"Transformers in Time Series: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07125","snapshot_observed_at":"2026-08-06T16:35:26.044462Z","title":"Transformers in time series: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.044462Z"},"links":{"cited_paper":"/paper/2202.07125","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:0255da56b5ef368642fd5749d81a05d3d71b20330fe6e392813f2bdf0f658048","observation_id":"e3888425-f112-476a-9895-df2041a59001","resolution":{"observed_at":"2026-08-06T16:35:26.044462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06119","last_updated":"2024-10-17T01:13:51Z","snapshot_observed_at":"2026-07-06T16:30:08.320348Z","submitted_at":"2023-10-09T19:52:22Z","title":"Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06119","snapshot_observed_at":"2026-08-06T16:35:26.066643Z","title":"Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.066643Z"},"links":{"cited_paper":"/paper/2310.06119","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:082375d8f6ad9b29ae96e40ecaab34491190d24f8375820f58eac71d9b63a08b","observation_id":"7e9c69a4-df69-4387-ba1a-64e0dc577446","resolution":{"observed_at":"2026-08-06T16:35:26.066643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20150","last_updated":"2025-08-18T05:01:29Z","snapshot_observed_at":"2026-08-05T03:15:49.797401Z","submitted_at":"2024-03-29T12:37:57Z","title":"TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20150","snapshot_observed_at":"2026-08-06T16:35:26.093417Z","title":"Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.093417Z"},"links":{"cited_paper":"/paper/2403.20150","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:be2d64cbee81c9284319f486d821f75e28405762cf49f2fc9570902365c1ef84","observation_id":"9a03fe0d-bcfa-4916-b7f2-0ffc2ad3d170","resolution":{"observed_at":"2026-08-06T16:35:26.093417Z","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":"2026-08-06T16:35:30.261536Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis,","venue":null,"work_id":"85a4b829-da0d-4e14-87df-dafb2ee355fa","year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.121114Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:159ec4a55ea3b811a8102c3e0749541ea995eb86518a80b84513ffc83ee8ff95","observation_id":"a1a40764-3d44-4966-8a9d-8bf1cf3757c2","resolution":{"observed_at":"2026-08-06T16:35:30.351232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:30.084406Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":"d2ba79a3-83d9-409a-9209-1f8122842303","year":2019},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.264496Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:e3e49e5f1e7e4d1e22a2045f3a1fcbbf342b6c2c83aa4d06dee40d8887d9ead2","observation_id":"4f695944-fb56-4b7d-ac8b-e77642730a35","resolution":{"observed_at":"2026-08-06T16:35:30.168966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:29.851679Z","title":"Learning a trajectory using adjoint functions and teacher forcing,","venue":null,"work_id":"691ada41-b9ec-4cf3-b383-ea45342484f5","year":1992},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.412113Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:185a3ce30693ac2b8f0312893b097f63a17c2d408155c7b771395f6ce87adf80","observation_id":"1ec8aa1b-bdd6-4e10-bf73-2989a2b5e7b9","resolution":{"observed_at":"2026-08-06T16:35:29.963681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:29.612575Z","title":"Professor forcing: A new algorithm for training recurrent networks,","venue":null,"work_id":"c0fe74b6-a45a-4b4b-9be0-06e069dbd4a4","year":2016},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.561283Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:c5188e88c689e6b0e8eaacafc446a574b9a929f8b3b53bbc966d7e4b7ea70aad","observation_id":"0256fe8f-ecc1-445f-9ecf-8cb1f41fcafa","resolution":{"observed_at":"2026-08-06T16:35:29.727229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:29.421675Z","title":"Reversible instance normalization for accurate time-series forecasting against distribution shift,","venue":null,"work_id":"ebba40c4-1e05-46d5-9d5c-051956f31dcc","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.688271Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:98796d37a0825ed546b36b1bda07e770f8eb2e85c4af483cdfdbc374f49d7df6","observation_id":"4ed33f21-4336-4568-8c36-c44eef56ac11","resolution":{"observed_at":"2026-08-06T16:35:29.524994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:29.258802Z","title":"Are transformers effective for time series forecasting?","venue":null,"work_id":"c3c24d05-2ce4-4af1-9eaa-93005a5faf6e","year":2022},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:26.909049Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:b19669d67c675a22484da38b995fbaa5daca0c0cd1eee5a63c061e8f5b040892","observation_id":"9e0d6a77-5a0b-46ea-b7cc-b0eedd19c446","resolution":{"observed_at":"2026-08-06T16:35:29.329049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:29.123327Z","title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,","venue":null,"work_id":"b589558d-6d82-4c98-a87f-604239adad71","year":2020},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.031045Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:e7b25610adb0ebabf2094293581dbe5654452812f3f619e4687b8c9d061cef76","observation_id":"ce34f604-2b74-4c81-9519-6313aea39750","resolution":{"observed_at":"2026-08-06T16:35:29.179416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:28.925541Z","title":"Unified training of universal time series forecasting transformers,","venue":null,"work_id":"d378aa7d-a399-4e50-9d80-5a9b827aef5f","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.201756Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:731e0d4bfefd5a261deb27b2e813004734a386f94eb1d468ec53dea9654f26bb","observation_id":"66990188-572f-4734-8819-8d44fe07a321","resolution":{"observed_at":"2026-08-06T16:35:29.053894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:28.724077Z","title":"One fits all: Power general time series analysis by pretrained lm,","venue":null,"work_id":"56b13c55-6724-433e-ad95-1d5e0a07182c","year":2023},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.347934Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:a170dca9a1c38be6b713505b7206a34018dec1d6d8e54c4123900f4f65cd121a","observation_id":"ac97064b-ada4-4040-b3f0-0d52b8a7d71d","resolution":{"observed_at":"2026-08-06T16:35:28.823727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:28.536737Z","title":"A decoder-only foundation model for time- series forecasting,","venue":null,"work_id":"62686c82-1de5-4190-8fbc-0e5fd9574100","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.613804Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:e2e6a8ba6be435c4d1b09619ff75a786460bd7984830cf3d071687d2a5bf2da8","observation_id":"b251a94f-b7bf-47f9-bb56-3d41c6fd08cd","resolution":{"observed_at":"2026-08-06T16:35:28.658209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:28.412667Z","title":"Timer: Generative pre-trained transformers are large time series models,","venue":null,"work_id":"7219202a-f8e7-4783-ab76-8b1a6c317230","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.791292Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:51ce56980f855384cb99ed5bc4892341fac4ac8b5ac2bd570a7d3fa86b7deff0","observation_id":"deac71cf-12e8-45b4-bf85-b878edb7d75b","resolution":{"observed_at":"2026-08-06T16:35:28.453858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:28.242355Z","title":"Detection of outliers using interquartile range technique from intrusion dataset,","venue":null,"work_id":"b95aa46b-4728-4ff0-aa1d-ccdbe0d954a7","year":2018},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:27.939208Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:3c27b74f4b016d6c3a0fb7bdf99f2f5a6d88fe53cdb96b564bc73d6f97791fce","observation_id":"e1b07135-0a7a-4ab6-a721-8eaa1057001d","resolution":{"observed_at":"2026-08-06T16:35:28.318398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T16:35:32.061503Z","title":"Available: https://www.sciencedirect.com/science/article/pii/ S0893608020300010","venue":null,"work_id":"c692f021-2f32-41ac-a004-db7af20929da","year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:24.864860Z"},"links":{"citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:3ce06583fb9f4d1efa1252eca43564ca7648ff61d774bf09a3ddf901813c1f55","observation_id":"5b4d5561-aafd-4eb1-91fb-b842aa262cf5","resolution":{"observed_at":"2026-08-06T16:35:32.172953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.01381","last_updated":"2022-06-20T06:58:05Z","snapshot_observed_at":"2026-08-06T06:19:36.912358Z","submitted_at":"2022-02-03T02:50:44Z","title":"ETSformer: Exponential Smoothing Transformers for Time-series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01381","snapshot_observed_at":"2026-08-06T16:35:25.081820Z","title":"Available: https://arxiv.org/abs/2202.01381","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T16:35:25.081820Z"},"links":{"cited_paper":"/paper/2202.01381","citing_paper":"/paper/2507.13043"},"observation_digest":"sha256:37bf3b9b4247d206a2af02152b09ea3279e4a1bff2caa74ad2bbb4d7c1478af1","observation_id":"3ecbc1ff-71a1-4661-852b-7e15014607bd","resolution":{"observed_at":"2026-08-06T16:35:25.081820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.13043","last_updated":"2025-07-17T12:16:04Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T16:29:39.516062Z","submitted_at":"2025-07-17T12:16:04Z","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":52},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.13043."}