{"as_of":"2026-08-07T10:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8fde84e63ba8f5b6f962429c49cba80155186202ac2bcaddb79d22f8f99a1f70","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:01:28.369282Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"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/2507.09445/citation-record","integrity":"/paper/2507.09445/integrity","json":"/paper/2507.09445/citation-record.json","paper":"/paper/2507.09445"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:01:23.492366Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.492366Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:3583793ca5c4bdf6ca8fe7e0f6896a32eb846b3ed7121097f2520be7b8e037f5","observation_id":"af90fe59-e8a2-4d9f-b7d0-48e9fa2839e6","resolution":{"observed_at":"2026-08-06T18:01:23.492366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.3555","last_updated":"2014-12-11T06:46:53Z","snapshot_observed_at":"2026-08-03T11:40:51.182181Z","submitted_at":"2014-12-11T06:46:53Z","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.3555","snapshot_observed_at":"2026-08-06T18:01:23.539643Z","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.539643Z"},"links":{"cited_paper":"/paper/1412.3555","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:7a6b5dc489641571482d40c64c0ebc4b728d2e86d267fa73bd9b9d95de4b35aa","observation_id":"b0e01c57-8d8f-465e-b564-1ddfa0b222f2","resolution":{"observed_at":"2026-08-06T18:01:23.539643Z","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":"2026-08-06T18:01:35.307208Z","title":"Deep state space models for time series forecasting,","venue":null,"work_id":"8578b267-a527-479b-aac4-cbeec0e0abbf","year":2018},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.635388Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:cef39a9435fd90927e0213a965e72037e5a6684b51aa8bd361edea06351d2b50","observation_id":"da93069f-cf43-4e45-a58c-fb18cb7050b3","resolution":{"observed_at":"2026-08-06T18:01:35.355171Z","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":"2026-08-06T18:01:35.195715Z","title":"Dilated recurrent neural networks,","venue":null,"work_id":"1ff49c92-3f12-4e89-9de4-515e39ce641d","year":2017},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.702253Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:4460253bcbbaa459c4ed1d15675390e99fae81c028435415c381a1815aebda1e","observation_id":"e0ae76fa-4c4a-4728-a278-c149939a2b16","resolution":{"observed_at":"2026-08-06T18:01:35.247165Z","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":"2026-08-06T18:01:35.083505Z","title":"Impact of covid-19 pandemic on electricity demand in the uk based on multivariate time series forecasting with bidirectional long short term memory,","venue":null,"work_id":"98b17591-6b13-48a9-a407-bb19256dc41b","year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.768926Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:b9db981842b75c1069dc9acf9e56bef62d2214f9bba47a046f45112642a4b91c","observation_id":"2c51d0a3-524c-4229-abd8-db2b51940538","resolution":{"observed_at":"2026-08-06T18:01:35.132037Z","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":"2026-08-06T18:01:34.976322Z","title":"Spatiotemporal attention for multivariate time series prediction and interpretation,","venue":null,"work_id":"f63e9b6d-f4b4-4854-9ee5-852b7f0dae6f","year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.834820Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:3a6547b368031775ad6d872b3a28ec588547df8c6098fd59a87ab9d1c82dfbf5","observation_id":"eb473eda-f731-4c75-9145-0ca34c0cd988","resolution":{"observed_at":"2026-08-06T18:01:35.033753Z","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":"2026-08-06T18:01:34.863077Z","title":"WITRAN: Water- wave information transmission and recurrent acceleration network for long-range time series forecasting,","venue":null,"work_id":"def8b8a9-f08e-4995-9707-c58214c2ae98","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:23.935362Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:aae43df0ec4455e38eb13593ed3f3c4da3731de8528dc265f4b9a464d2c02f76","observation_id":"8419994a-3896-4182-abb7-273859fd8bf6","resolution":{"observed_at":"2026-08-06T18:01:34.922209Z","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":"2026-08-06T18:01:34.634390Z","title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks,","venue":null,"work_id":"1ba07286-2aaa-4cb8-baee-c46f7b1799d6","year":2020},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.001630Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:a59032faaabf65973335bd87859770ff37426558250154c2202a5347b80d0d92","observation_id":"b3d9c91a-3206-41bb-89c8-bb81d5867c0a","resolution":{"observed_at":"2026-08-06T18:01:34.788550Z","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":"2026-08-06T18:01:34.353409Z","title":"FiLM: Frequency improved legendre memory model for long-term time series forecasting,","venue":null,"work_id":"05676f0a-70a4-4ae0-a908-5a393f81d5b8","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.101727Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:7201e4a9ca2d6f05c0b36555d8fe5dada076066599b651ec170121f3ce887147","observation_id":"7a45bf02-7689-46d3-ba07-42f8bd52ac1a","resolution":{"observed_at":"2026-08-06T18:01:34.486974Z","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":"2026-08-06T18:01:34.246203Z","title":"ModernTCN: A modern pure convolution structure for general time series analysis,","venue":null,"work_id":"cf28e981-6c1e-4b17-958c-8e0c11e57ba6","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.169196Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:1c9f4c0bacd320971f7490306daad29ef0e2ccae89c5ca2df7880674659ff13d","observation_id":"c54b4ab9-77c3-46fc-a19f-2f7d17f27c98","resolution":{"observed_at":"2026-08-06T18:01:34.342578Z","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":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-07-06T06:26:27.965096Z","submitted_at":"2018-03-04T00:20:29Z","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.01271","snapshot_observed_at":"2026-08-06T18:01:24.235049Z","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.235049Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:b47f392ea0895bb7b9ac2794f50dd77612b743735fd1ebc383eb9d6506d90ba0","observation_id":"ad0b725d-a9e8-46a5-b2d7-a41922ce7bce","resolution":{"observed_at":"2026-08-06T18:01:24.235049Z","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":"2026-08-06T18:01:33.977402Z","title":"SCINet: Time series modeling and forecasting with sample convolution and interaction,","venue":null,"work_id":"44ee4e30-fec4-4f04-868a-bfa0fdebdd76","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.335074Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:51f64ec547359ebcd3bf4083b05dd6fdeaeb73036c1f977f471e26ad0889abb7","observation_id":"5ea21c8b-2f33-431b-90cd-01443556f801","resolution":{"observed_at":"2026-08-06T18:01:34.095931Z","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":"2026-08-06T18:01:33.668712Z","title":"MICN: Multi-scale local and global context modeling for long-term series forecasting,","venue":null,"work_id":"9ae96220-e4de-4e1e-8684-9580e5fa02d3","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.435287Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:e7ad536397e364070a8d503b6cb87c27020ac5ebe373a0eba2eeabe3e2577d3f","observation_id":"46335258-7dd4-46b1-af45-784f344cb237","resolution":{"observed_at":"2026-08-06T18:01:33.807377Z","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":"2026-08-06T18:01:33.408639Z","title":"Unsupervised scalable representation learning for multivariate time series,","venue":null,"work_id":"8d4fa74c-7a5c-4cc9-b124-13050dd2d587","year":2019},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.535124Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:10c8298b8a36d092f3e7b6aa82d384d73dc9e09dd178877d52fef44b59815df7","observation_id":"bf178991-ef4a-49e9-8e56-8cd3b4416e66","resolution":{"observed_at":"2026-08-06T18:01:33.536447Z","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":"2026-08-06T18:01:33.213579Z","title":"Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting,","venue":null,"work_id":"fb633b76-551f-4d61-97a4-24e53536f417","year":2019},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.601747Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:eb0d81988aa2d6b3bc17ff6cd1022a94889f7a8198a455041ecd08f17b85eed8","observation_id":"7bddcedf-d4f0-4a75-a7cf-c9fad24da62c","resolution":{"observed_at":"2026-08-06T18:01:33.315286Z","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":"2026-08-06T18:01:32.885251Z","title":"TimesNet: Temporal 2d-variation modeling for general time series analysis,","venue":null,"work_id":"fe541eb2-ac06-4b10-9b12-484acc430e7e","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.701913Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:61a723ffd7f39cbd447032857073e0de303afe032e6898ccd55a2207c9389e4e","observation_id":"1461af2f-f273-41e8-ade4-2a1a11e51476","resolution":{"observed_at":"2026-08-06T18:01:33.039613Z","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":"2405.14616","last_updated":"2024-05-23T14:27:07Z","snapshot_observed_at":"2026-08-05T11:50:43.178704Z","submitted_at":"2024-05-23T14:27:07Z","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14616","snapshot_observed_at":"2026-08-06T18:01:24.769483Z","title":"Timemixer: Decomposable multiscale mixing for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.769483Z"},"links":{"cited_paper":"/paper/2405.14616","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:0b7d8a989765146ec188f31b9cf37f454740123c03259bb4440ae1f035a8e1af","observation_id":"fa17875f-4cd7-415a-9128-1abd06f4fe84","resolution":{"observed_at":"2026-08-06T18:01:24.769483Z","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":"2026-08-06T18:01:32.591525Z","title":"Unlocking the power of patch: Patch-based mlp for long-term time series forecasting,","venue":null,"work_id":"e9b018f9-cc35-4e42-942f-96cfe490ec06","year":2025},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.869604Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:df2fe0a3e3f408826790868e3184463fe82e5719882396640c7e60b9e7ff412b","observation_id":"496cbd02-604d-41c1-822f-3fd246cbb068","resolution":{"observed_at":"2026-08-06T18:01:32.712076Z","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":"2303.06053","last_updated":"2023-09-11T11:19:49Z","snapshot_observed_at":"2026-08-06T06:19:20.551499Z","submitted_at":"2023-03-10T16:41:24Z","title":"TSMixer: An All-MLP Architecture for Time Series Forecasting","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.06053","snapshot_observed_at":"2026-08-06T18:01:24.969257Z","title":"Tsmixer: An all-mlp architecture for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:24.969257Z"},"links":{"cited_paper":"/paper/2303.06053","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:d6695ade3aa2621b726fb5dc42f737130af8babd97c422dfb67ef7e012ead71a","observation_id":"3a526238-4d2b-4ba5-9a82-0b4632d95471","resolution":{"observed_at":"2026-08-06T18:01:24.969257Z","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":"2026-08-06T18:01:32.346032Z","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,","venue":null,"work_id":"eebd6a35-5d29-46a5-9f44-45a54885f194","year":2019},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.070247Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:38e1f8f17d97eb4d5d10b065913ba77933fd4c287d0ef0fa06543f5531d4dd95","observation_id":"c7e86939-e68e-4827-bbb9-960075ffb273","resolution":{"observed_at":"2026-08-06T18:01:32.474207Z","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":"2026-08-06T18:01:32.154603Z","title":"N-HiTS: Neural hierarchical interpolation for time series forecasting,","venue":null,"work_id":"2893cbd4-1a34-4fce-94b1-496cc11abc2d","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.170005Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:1921aa91b13e3e98b466af18edd30b3f95c2d9873ea36ff6d03fc996c0f957f0","observation_id":"ed6c2ef7-5217-4c4f-96e9-879e0281eb03","resolution":{"observed_at":"2026-08-06T18:01:32.269075Z","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":"2026-08-06T18:01:31.956799Z","title":"Frequency-domain mlps are more effective learners in time series forecasting,","venue":null,"work_id":"20bf2fc7-d114-4d5c-8c79-d6abcc557008","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.303880Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:3aa52c247c1a9ba3150dc1f027cd944fea814b23295cb7532e3f6e1d2affe0da","observation_id":"6e4ef505-7f28-4b5e-a2c0-f62d5197de88","resolution":{"observed_at":"2026-08-06T18:01:32.044817Z","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":"2026-08-06T18:01:31.777234Z","title":"Attention is all you need,","venue":null,"work_id":"d2c8a94e-1f48-417a-9fb5-fab985201c94","year":2017},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.405201Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:88bceecb3df85baa400ad55e5f37d3708d8ab070362834a7decb9bae17747bff","observation_id":"32087f08-fd6f-47d4-a536-678701fa9b83","resolution":{"observed_at":"2026-08-06T18:01:31.859441Z","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":"2026-08-06T18:01:31.622783Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":"bde5ee58-1542-45e5-aee9-91736da6858a","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.504686Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:26079b4355079c5a6b80b8ad84da4b188e01e304b4ec7a23c05d32bc983c533e","observation_id":"ba94eaf3-9f51-4af1-aff3-cd522ea28f85","resolution":{"observed_at":"2026-08-06T18:01:31.681657Z","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":"2026-08-06T18:01:31.530897Z","title":"BasisFormer: Attention-based time series forecasting with learnable and interpretable basis,","venue":null,"work_id":"184e27cd-cf2d-43be-a961-3a5c73d1b43a","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.638518Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:0f65c552a2338ae28b4d8fd67bde9346d773f780a4ee95898036e5d3957029a1","observation_id":"9750ed46-c282-487a-9649-692255975031","resolution":{"observed_at":"2026-08-06T18:01:31.542222Z","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":"2026-08-06T18:01:31.311267Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":"783c4182-279b-4a1b-b728-50af7d8245c3","year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.754625Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:c0db63c9e1d599ad33a447daa008ce029547fc5ab099100254eb68b927bd9473","observation_id":"c4d3b172-08a0-413e-a165-3ed42d6a3ade","resolution":{"observed_at":"2026-08-06T18:01:31.415164Z","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":"2026-08-06T18:01:25.839080Z","title":"Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.839080Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:d687239ca24e915cd674bd38ee3d98cf7d9f02b6c9d9d4dcfd1232386906a267","observation_id":"e1f010ba-6aa1-4e37-916a-9e4c6b475d4e","resolution":{"observed_at":"2026-08-06T18:01:25.839080Z","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":"2026-08-06T18:01:25.939264Z","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:25.939264Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:a92207647fb77c9d98dfe6c117bded8a3a1ce8aa657c36fb2b39746e8885b5c8","observation_id":"ec1336f3-a855-4e2d-8110-259ffdf50751","resolution":{"observed_at":"2026-08-06T18:01:25.939264Z","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":"2026-08-06T18:01:30.995309Z","title":"Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,","venue":null,"work_id":"484816a8-4224-48f1-b952-6976a85828ad","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.073090Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:73492b34d5d00bc7adde1284d3ee725aa68241572da8565e573d4d30738bc469","observation_id":"a4107e77-8f71-4072-98d2-bd75508b7007","resolution":{"observed_at":"2026-08-06T18:01:31.132360Z","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":"2026-08-06T18:01:30.753618Z","title":"InParformer: evolutionary decomposition transformers with interactive parallel at- tention for long-term time series forecasting,","venue":null,"work_id":"7c059fff-4101-49a7-bcf8-550cd591016a","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.205541Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:e204e40f569e9751ed8c66c2c0609de3ae74be0d4cb9eb7a0ff633193b9ff677","observation_id":"ff64119e-dc65-4e3f-9ba3-bbdb879f7188","resolution":{"observed_at":"2026-08-06T18:01:30.873642Z","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":"2026-08-06T18:01:30.457482Z","title":"An encoder–decoder architecture with fourier attention for chaotic time series multi-step prediction,","venue":null,"work_id":"a269fcf7-f54f-4fd7-b00a-43e06183c377","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.305766Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:c46159b3c07605f77e7bf397691d31be1576589038507df510e572b8d6e49f7b","observation_id":"08f29938-2626-433c-8de8-b7d0c7db9f4f","resolution":{"observed_at":"2026-08-06T18:01:30.584394Z","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":"2026-08-06T18:01:30.260768Z","title":"Temporal chain network with intuitive attention mechanism for long-term series forecasting,","venue":null,"work_id":"ba0c7a47-05c0-494a-ba7e-49848dac161a","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.405610Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:4b814bb031e04a5a0d833ac72f8ec6ca2b993fff4997258b14f434ebea1e9738","observation_id":"bf60e706-7854-45f8-8e2b-72f4daafc626","resolution":{"observed_at":"2026-08-06T18:01:30.350482Z","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":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-05T15:23:02.346614Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T18:01:26.539715Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.539715Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:63274d932f90fc47e95c1973a2f7f82bc67d7418541e2611b390812fae8bf72b","observation_id":"6a9f81e4-710a-4225-88e1-6fb4db85126c","resolution":{"observed_at":"2026-08-06T18:01:26.539715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T18:01:26.634168Z","title":"Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.634168Z"},"links":{"cited_paper":"/paper/2402.05956","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:bebde55a083865f2254ea32e4cc1cc5c879db289609f1a1de52ee43efe5e66fa","observation_id":"2567d205-9f03-4178-89ad-52be162d99c0","resolution":{"observed_at":"2026-08-06T18:01:26.634168Z","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":"2026-08-06T18:01:30.069451Z","title":"iTrans- former: Inverted transformers are effective for time series forecasting,","venue":null,"work_id":"4f8f8af0-e594-4ac3-a9a3-fed1a5b21281","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.700880Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:408d2ffd804c366c3c58a1f86b85212df49e4074e14f2e41934550aeea55d397","observation_id":"e5fe80b2-6944-4439-816c-dd6f67fcd047","resolution":{"observed_at":"2026-08-06T18:01:30.181451Z","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":"2412.10859","last_updated":"2025-01-10T14:28:32Z","snapshot_observed_at":"2026-07-06T20:07:05.953059Z","submitted_at":"2024-12-14T15:15:17Z","title":"DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.10859","snapshot_observed_at":"2026-08-06T18:01:26.797403Z","title":"Duet: Dual clustering enhanced multivariate time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.797403Z"},"links":{"cited_paper":"/paper/2412.10859","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:b6a952fff758060df16ffb0f377bf33ddc692b1468cfccb204c3dfdf83205675","observation_id":"100c3f5f-115e-4d2e-99f0-243cb3fa8331","resolution":{"observed_at":"2026-08-06T18:01:26.797403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04803","last_updated":"2025-03-02T11:22:35Z","snapshot_observed_at":"2026-07-06T19:28:52.383175Z","submitted_at":"2024-10-07T07:27:39Z","title":"Timer-XL: Long-Context Transformers for Unified Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04803","snapshot_observed_at":"2026-08-06T18:01:26.861413Z","title":"Timer-xl: Long- context transformers for unified time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.861413Z"},"links":{"cited_paper":"/paper/2410.04803","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:6bffac15450925c8ed218003732cb847862c0864573f3f8e1de289dbdfb25cdb","observation_id":"1a5309be-5ef1-43fe-a197-2f3210c0c74e","resolution":{"observed_at":"2026-08-06T18:01:26.861413Z","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":"2026-08-06T18:01:29.936897Z","title":"Sepformer-based models: More efficient models for long sequence time-series forecasting,","venue":null,"work_id":"27f7f1e4-f24a-436d-85e8-a062ea741596","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.961841Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:00d73efd7f2adaa1c49065defd5ba8d3f7d40121ccbf2187acee6c41eb9867c7","observation_id":"66315615-2284-46af-96e8-204ea9ef4e3e","resolution":{"observed_at":"2026-08-06T18:01:30.005865Z","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":"2026-08-06T18:01:27.051646Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.051646Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:da3bad244ccdfaddcb92b2d908ae408ca7d1125390b470fbdc9d88b391feb7dd","observation_id":"cdf832f9-3357-41e0-b758-810bdb24a196","resolution":{"observed_at":"2026-08-06T18:01:27.051646Z","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":"2026-08-06T18:01:29.785608Z","title":"CrossGNN: Confronting noisy multivariate time series via cross interaction refinement,","venue":null,"work_id":"6123ff06-3a05-43b6-ad8d-f4340b8cfcf5","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.121896Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:1f66fe6cfca2a9bd634214d6c4c9ec4420291bb8cae07429771d2c9843041c50","observation_id":"edf9127f-1a67-443e-b454-7c7d67d3589a","resolution":{"observed_at":"2026-08-06T18:01:29.858776Z","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":"2311.06190","last_updated":"2023-11-10T17:13:26Z","snapshot_observed_at":"2026-07-06T16:45:47.395736Z","submitted_at":"2023-11-10T17:13:26Z","title":"FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06190","snapshot_observed_at":"2026-08-06T18:01:27.177803Z","title":"FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.177803Z"},"links":{"cited_paper":"/paper/2311.06190","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:1420c08b3ae7ce438232923cd3f82affc4e7f67d4682eb560872467afbe2eac6","observation_id":"3b71aedb-64cb-4960-a3be-238529667222","resolution":{"observed_at":"2026-08-06T18:01:27.177803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-08-06T18:01:27.272133Z","title":"Graph deep learning for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.272133Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:6a43288b2f93a9d8409d56daffe477ea2d88381c6515bf92d3e50f6c19aab506","observation_id":"4de3d25f-dfa5-4d28-b0e3-013853517c0b","resolution":{"observed_at":"2026-08-06T18:01:27.272133Z","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":"2026-08-06T18:01:29.673191Z","title":"Adaptive dependency learning graph neural networks,","venue":null,"work_id":"17d5b342-bd26-41a3-b37e-8e021439eec6","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.346676Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:75e4f43992804f1e15de619686183e907e7bdcb417f5d7dda642194462864dca","observation_id":"4e897ce0-59a6-487c-9b14-dc7f40127c53","resolution":{"observed_at":"2026-08-06T18:01:29.725281Z","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":"2026-08-06T18:01:29.535674Z","title":"Multivariate time series forecasting with dynamic graph neural odes,","venue":null,"work_id":"ca3519a1-4cee-40fe-a1a0-b6c9344fa90f","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.434356Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:43d333830273a2bbd6fe501e3eb8ae3f688e00dd4f8c0389c2e45b3b67f84aa9","observation_id":"afe2cef6-31c4-41ff-b9e1-cd3fd7afb175","resolution":{"observed_at":"2026-08-06T18:01:29.593842Z","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":"2026-08-06T18:01:29.404557Z","title":"Mixmamba: Time series modeling with adaptive expertise,","venue":null,"work_id":"cea0b8ad-4c45-496e-b5aa-48b88906f33e","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.529599Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:11df54712d694da95b27d3a36b82a352ec8af16b492248f2f74050787d7df0f3","observation_id":"9487eab4-a579-423f-8f0f-97454eb4b9fe","resolution":{"observed_at":"2026-08-06T18:01:29.459007Z","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":"2026-08-06T18:01:29.298890Z","title":"Is mamba effective for time series forecasting?","venue":null,"work_id":"cd395f72-dcd2-4c04-9e05-6f613d6ee1fa","year":2025},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.591376Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:d3715a3184c43cccd677d63b5ed0a512cabcb7abd5ed6ac231212339994e7c2e","observation_id":"a25efd9f-5631-42dd-856f-23fb1c578114","resolution":{"observed_at":"2026-08-06T18:01:29.357313Z","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":"2406.05316","last_updated":"2024-09-26T03:54:15Z","snapshot_observed_at":"2026-08-07T08:20:40.181174Z","submitted_at":"2024-06-08T01:32:44Z","title":"CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05316","snapshot_observed_at":"2026-08-06T18:01:27.684462Z","title":"Cmamba: Channel correlation enhanced state space models for multivariate time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.684462Z"},"links":{"cited_paper":"/paper/2406.05316","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:f1871d99158eefcba8502cd69851fcf60becdb3199ade9991b1fa7048b9abd8c","observation_id":"50f07835-7a3b-4e31-8ec5-6271d9333352","resolution":{"observed_at":"2026-08-06T18:01:27.684462Z","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":"2026-08-06T18:01:29.155774Z","title":"Are transformers effective for time series forecasting?","venue":null,"work_id":"477f2c18-5db7-47e8-9c30-23b8c023b4f6","year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.782601Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:3cd8036cafa209633f3b25b9634d3c18327b33fd12964d9884493c1cefbfbc89","observation_id":"ff21c82f-38bc-4b62-b88d-1351dbd993f1","resolution":{"observed_at":"2026-08-06T18:01:29.209270Z","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":"2307.03756","last_updated":"2024-01-05T06:49:04Z","snapshot_observed_at":"2026-07-06T15:51:35.559366Z","submitted_at":"2023-07-06T15:01:58Z","title":"FITS: Modeling Time Series with $10k$ Parameters","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03756","snapshot_observed_at":"2026-08-06T18:01:27.850889Z","title":"FITS: Modeling time series with 10k parameters,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.850889Z"},"links":{"cited_paper":"/paper/2307.03756","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:e87db91b810a5ca1d72740f47312bdefc8fafdef1edca738f1cbcd83e056bfb0","observation_id":"f9974106-33fe-41ad-8da8-2e4ae8678d3d","resolution":{"observed_at":"2026-08-06T18:01:27.850889Z","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":"2026-08-06T18:01:29.024894Z","title":"FL-Net: A multi-scale cross-decomposition network with frequency external attention for long-term time series forecasting,","venue":null,"work_id":"73bdbfc1-4aee-4f0e-9d17-595e86648663","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.904414Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:7d89eeef52061b45bc2b2d32e08b4978cbce581d4da7a00b76be9f3062c22b32","observation_id":"c8e76341-6e65-4758-b33b-26a1c5827c41","resolution":{"observed_at":"2026-08-06T18:01:29.087624Z","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":"2026-08-06T18:01:28.932464Z","title":"Rethinking fourier transform from a basis functions perspective for long-term time series forecasting,","venue":null,"work_id":"eea0ec50-6b7b-452d-bdb9-38db91727c65","year":2024},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.979743Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:8bed3390fa795fee2a5c19a2465be1ce5c0478dcbaf842e22b458f158c77dece","observation_id":"eb00d7fd-5605-4474-b26f-a6fbadc42d3d","resolution":{"observed_at":"2026-08-06T18:01:28.960154Z","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":"2026-08-06T18:01:28.827222Z","title":"En- hancing the locality and breaking the memory bottleneck of transformer on time series forecasting,","venue":null,"work_id":"2d99df23-424b-4889-9d14-08fa9d055de2","year":2019},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.026639Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:dd9567f520a081a503059c9af5ad77ce34e8e7cee1344186bef35151dcdcbc74","observation_id":"24419ae6-fb91-41ba-8365-566a10bde347","resolution":{"observed_at":"2026-08-06T18:01:28.876293Z","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":"2026-08-06T18:01:28.734810Z","title":"Variational hierarchical n-beats model for long-term time-series forecasting,","venue":null,"work_id":"7cb7cb2e-cd2d-4044-be87-180bae77911c","year":2025},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.085976Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:0ecff23889c7c84f16a3550f1da61afa569b15ac5474ce0e2d79b968c432642c","observation_id":"a768e04b-3335-4356-aa16-1b6be550aedb","resolution":{"observed_at":"2026-08-06T18:01:28.783947Z","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":"2026-08-06T18:01:28.157903Z","title":"Reversible instance normalization for accurate time-series forecasting against distri- bution shift,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.157903Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:ef8bda83dcc9bb94199cc63ecba3190f75d1c1e5f1386ea1ff0447ea35723e8c","observation_id":"1355f632-d3f8-48b8-98d7-ddb8c30e627c","resolution":{"observed_at":"2026-08-06T18:01:28.157903Z","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":"2026-08-06T18:01:28.212347Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.212347Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:f30ecd4964919d9fd544c7d23adab9d15369b9992a9ae60568c5ff1cdd65884b","observation_id":"bfb094d9-5f7d-4699-b16b-de252dbe5d73","resolution":{"observed_at":"2026-08-06T18:01:28.212347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01186","last_updated":"2022-07-04T04:03:00Z","snapshot_observed_at":"2026-08-04T07:41:23.620753Z","submitted_at":"2022-07-04T04:03:00Z","title":"Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01186","snapshot_observed_at":"2026-08-06T18:01:28.269078Z","title":"Less is more: Fast multivariate time series forecasting with light sampling- oriented mlp structures. arxiv 2022,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.269078Z"},"links":{"cited_paper":"/paper/2207.01186","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:90e07494188a26019854c64caf810fc67354bfb3923439a7dce2471100690617","observation_id":"d8bd6703-d5af-45ff-a9b8-1de243825491","resolution":{"observed_at":"2026-08-06T18:01:28.269078Z","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":"2026-08-06T18:01:28.585181Z","title":"Non-stationary transformers: Exploring the stationarity in time series forecasting,","venue":null,"work_id":"a5f153de-2031-4d92-8411-c48463b06e32","year":2022},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:28.369282Z"},"links":{"citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:95a663f0dc8d09bd19b1d90afdf336493f348fb9b2b89ec06ec1bb888cf7e528","observation_id":"baaee81a-84f3-4fc8-ab30-8e63257af314","resolution":{"observed_at":"2026-08-06T18:01:28.664604Z","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"}}],"paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":57},"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 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2507.09445."}