{"as_of":"2026-08-17T09:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd413694f20ab66022dcdfac1dbdf4b1ff281b4ec208c898f9d7d49d22bb2121","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:13:41.689634Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2411.17382/citation-record","integrity":"/paper/2411.17382/integrity","json":"/paper/2411.17382/citation-record.json","paper":"/paper/2411.17382"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:13:42.067809Z","title":"Doubleadapt: A meta-learning approach to incremental learning for stock trend forecasting,","venue":null,"work_id":"0e767503-7aaa-442c-8361-bfdcddaf0205","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.555288Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:43f04532c5892ea416a00ddc05c197fab980c90b833b0e8046768c7ec6ab4c16","observation_id":"f7675701-0880-4dd3-8d79-fd68834c0467","resolution":{"observed_at":"2026-08-12T12:13:42.070869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:42.058949Z","title":"Ai in finance: challenges, techniques, and opportunities,","venue":null,"work_id":"ac47d219-3654-42ae-b126-91ee01e3576f","year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.560180Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:2eaafdae9d509ac45d35bafd3c85f742c92a2105d751839a0d7347f500660d01","observation_id":"46034437-9a6e-42ce-9604-df75a249fd5f","resolution":{"observed_at":"2026-08-12T12:13:42.061653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:42.048489Z","title":"An efficient equilibrium optimizer with support vector regression for stock market prediction,","venue":null,"work_id":"2ec29e3d-6eb0-4afc-983d-aa3f216dcc20","year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.564127Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:857ea305e5897b115a9048debba37c2aef4c8f4ca741e01ea51e3350e5b90be5","observation_id":"a8bab003-364b-4c9c-9221-88d640541b59","resolution":{"observed_at":"2026-08-12T12:13:42.051552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.568297Z","title":"Accurate medium-range global weather forecasting with 3d neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.568297Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:f298559d5d5f525c9c7dfd69a91ab80ce3deb3a5e2436de6f1563c553b0418bd","observation_id":"b5ec1228-941c-4756-9636-9caa55e976d3","resolution":{"observed_at":"2026-08-12T12:13:41.568297Z","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":"2026-08-12T12:13:42.031959Z","title":"Data-driven predictive control for smart hvac system in iot-integrated buildings with time-series forecasting and reinforcement learning,","venue":null,"work_id":"e0fc0177-35b3-4f98-b042-f4c0897de3ad","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.571860Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:ee292df9ee4d9b80c08804900a0066c2706e4fad74d0b884fb3a5c4ab9bbfb4c","observation_id":"3a756064-4190-4492-a1f3-a18313f3d7b3","resolution":{"observed_at":"2026-08-12T12:13:42.036420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.575541Z","title":"Spatio-temporal meta-graph learning for traffic forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.575541Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:b5c44f9538bd84b7d792913f8a0b23a03f4aa1a08b691d03234129730372eb68","observation_id":"29d7ea6f-d120-4370-8038-bfa6bc4f08a3","resolution":{"observed_at":"2026-08-12T12:13:41.575541Z","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":"2026-08-12T12:13:42.014445Z","title":"Time series prediction using deep learning methods in healthcare,","venue":null,"work_id":"8e83c665-fb99-4cb0-9a55-cff3113a2a05","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.578967Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:80adecf9e0c939d8b8f5050df1680d8a62504d48317e03c2c9d3a7eed4bb1c24","observation_id":"8c97193d-8696-4bcd-bcf6-656196ae5292","resolution":{"observed_at":"2026-08-12T12:13:42.018327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:42.003421Z","title":"Std: a seasonal-trend-dispersion decomposition of time se- ries,","venue":null,"work_id":"21ed02de-a350-4110-8f44-1d4ff6b47125","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.582051Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:45d2bb7208f04e46823a53ce84a535e9aef6456b167fcbe8f84927929a6d962f","observation_id":"38459580-55d9-48f0-9f6a-c0ad7d563022","resolution":{"observed_at":"2026-08-12T12:13:42.007188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.990439Z","title":"A rnn based time series approach for fore- casting turkish electricity load,","venue":null,"work_id":"700a3fe8-9ee5-47d4-9d57-8b9f3c11ccf3","year":2018},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.585528Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:d5cb7ac42593e699f65980d4d59cbc8644ef3122df17d29381e9ece0a0cc4334","observation_id":"556cf3d6-1325-449a-beba-ec0c18739852","resolution":{"observed_at":"2026-08-12T12:13:41.995102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.977275Z","title":"Ngcu: A new rnn model for time-series data prediction,","venue":null,"work_id":"15463677-6c4a-429d-a209-3d34ca343218","year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.588613Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:13ae6f21d1c56bae129af365a8b0719ec811503379a34f5bb1d0ec949c9de873","observation_id":"a1c8f8a3-a436-405a-9d94-6e30a3cde154","resolution":{"observed_at":"2026-08-12T12:13:41.980888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.962777Z","title":"The performance of lstm and bilstm in forecasting time series,","venue":null,"work_id":"a996b57d-58a7-4d67-8541-cf6c26d19c62","year":2019},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.592157Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:eb0a73fe888ea3adaaff46e89efc94187e9bfece457b829a95581bb2f98e2500","observation_id":"f8106c3b-811f-442f-8f49-e3b16f66e9c5","resolution":{"observed_at":"2026-08-12T12:13:41.967557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.952748Z","title":"U- net-lstm: time series-enhanced lake boundary prediction model,","venue":null,"work_id":"6a5e014a-016b-40ad-807a-fdffdaa1fe89","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.596107Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:bee52b5d272155cfde423a29044ffe1ec1f9a8cf0f82faa0e18d5d76637e8636","observation_id":"57de25f2-0c83-4c50-84ef-200fa91e4ff9","resolution":{"observed_at":"2026-08-12T12:13:41.956320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.940991Z","title":"A cnn–lstm model for gold price time-series forecasting,","venue":null,"work_id":"c651597b-58ff-4b9e-9e46-1ab9f51574eb","year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.599424Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:fb4ed30cf21d1936b8979d85e390b6b09635f332e08377f3adc07fff750065c6","observation_id":"359bc389-f74b-4b1b-b979-9a38af04a5ce","resolution":{"observed_at":"2026-08-12T12:13:41.944944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.931124Z","title":"Prediction for time series with cnn and lstm,","venue":null,"work_id":"1a17f700-6178-4404-a724-20658bfb8648","year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.602860Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:4e28d5fe989a62871fb8548b06b2ca69948f2146d85d53fc8c9007038615ed39","observation_id":"864392e0-2a80-40bd-b866-f61045a961ef","resolution":{"observed_at":"2026-08-12T12:13:41.933782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.921170Z","title":"Ts2vec: Towards universal representation of time series,","venue":null,"work_id":"0582420d-ae71-4544-a165-a941a47b1a04","year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.606695Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:21da2ce2a93232936c52ec18331fda95e3926543e316af15d8b9e117b5b74129","observation_id":"213ab821-2d7b-4bc8-8afc-8e5d8de217a1","resolution":{"observed_at":"2026-08-12T12:13:41.924948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04486","last_updated":"2024-05-09T10:11:23Z","snapshot_observed_at":"2026-08-16T14:54:26.194856Z","submitted_at":"2023-10-06T15:45:28Z","title":"T-Rep: Representation Learning for Time Series using Time-Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04486","snapshot_observed_at":"2026-08-12T12:13:41.611073Z","title":"T-rep: Representa- tion learning for time series using time-embeddings,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.611073Z"},"links":{"cited_paper":"/paper/2310.04486","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:9bcdb7561d60b98beda35beb53455e3662cf049557f4883ee1881bb9b6e8bc93","observation_id":"65f7f3e7-ee3d-4699-8db3-143f603d98c1","resolution":{"observed_at":"2026-08-12T12:13:41.611073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18205","last_updated":"2024-11-12T00:31:30Z","snapshot_observed_at":"2026-08-16T15:43:42.094225Z","submitted_at":"2023-03-31T16:59:40Z","title":"Simple Contrastive Representation Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18205","snapshot_observed_at":"2026-08-12T12:13:41.615682Z","title":"Simts: rethinking contrastive representation learning for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.615682Z"},"links":{"cited_paper":"/paper/2303.18205","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:2613623932d38a8b3c2da9e146675064c093c126882d74500015d457f3722672","observation_id":"6fc9a6d0-0846-4534-99ed-d0c35f12bac6","resolution":{"observed_at":"2026-08-12T12:13:41.615682Z","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":"2026-08-12T12:13:41.620052Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.620052Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:53ae8035acae69f0bf515f53dedff04f54c97f3624f9aa9785f56848a549dad7","observation_id":"d8b1c491-ebde-4c5a-82b4-26f85238530d","resolution":{"observed_at":"2026-08-12T12:13:41.620052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-08-17T01:22:50.943392Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-12T12:13:41.623528Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.623528Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:f34b1df254a583dcdd76ab04adbd436412b7c4f95a8b77af07045fd782c50ad9","observation_id":"6c47a6a7-20ea-4b9e-ae5f-7242e6740c9a","resolution":{"observed_at":"2026-08-12T12:13:41.623528Z","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":"2026-08-12T12:13:41.905528Z","title":"Carla: Self-supervised contrastive representation learning for time se- ries anomaly detection,","venue":null,"work_id":"e82ef70d-3a0e-4ba0-9a4b-6460cc745d7b","year":2025},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.628024Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:ef7c6323906a5e08dcd3f1eed4cc7f5aa39b5c4a0ca23018afb4815d0c9e4c0d","observation_id":"66c708a9-6d7b-4202-96ed-e3e820c6e54e","resolution":{"observed_at":"2026-08-12T12:13:41.909025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.01575","last_updated":"2022-05-05T08:48:46Z","snapshot_observed_at":"2026-08-16T17:23:52.740024Z","submitted_at":"2022-02-03T13:17:38Z","title":"CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01575","snapshot_observed_at":"2026-08-12T12:13:41.632439Z","title":"Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.632439Z"},"links":{"cited_paper":"/paper/2202.01575","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:397f20bb8822f4e133015a7e130771514e60fe9f1fe7bc8508a7b9059a63ba61","observation_id":"78532c09-7e4c-4d3b-8abe-8af5cb8d8f8e","resolution":{"observed_at":"2026-08-12T12:13:41.632439Z","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":"2026-08-12T12:13:41.636180Z","title":"Fouriergnn: Rethinking multivariate time series forecast- ing from a pure graph perspective,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.636180Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:2a883e5e7e1e94c496703ad7fe0dd782a50aa77a6e50ff0e48e25b469cdc9cd2","observation_id":"913981db-d8d2-4a35-86cc-17b527345e8e","resolution":{"observed_at":"2026-08-12T12:13:41.636180Z","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":"2026-08-12T12:13:41.886274Z","title":"Crossgnn: Confronting noisy multivariate time series via cross interaction refinement,","venue":null,"work_id":"60e407cd-abbd-46d7-a6f2-9d6b80b316fd","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.641050Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:30df4d2525c8b6b57ce683b65ae69d25056b0a3c6d2cde56c808deffcbb848d6","observation_id":"0d27d2c0-eaed-4198-b8b0-7ea541afe435","resolution":{"observed_at":"2026-08-12T12:13:41.889951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.644775Z","title":"Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.644775Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:da73caff5e014509131b536865e805b2b04a3f62c53b5e69e00dfc45099a8ead","observation_id":"d42c854d-8389-4703-bb9d-e4d78c27e01d","resolution":{"observed_at":"2026-08-12T12:13:41.644775Z","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":"2026-08-12T12:13:41.648307Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.648307Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:8e0822eaccf0e7744f6a8b2c2e065ea023d0479932fa46ee4e4679f6761cf9f0","observation_id":"74327b14-8a8e-469b-8389-7c77fe17d2da","resolution":{"observed_at":"2026-08-12T12:13:41.648307Z","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":"2026-08-12T12:13:41.862048Z","title":"Unsupervised data augmentation for consistency training,","venue":null,"work_id":"93849475-f276-49aa-9f37-9d68cdea8714","year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.652852Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:9f33037d78ea4e960a45b18c679bfbf2dc72b7fa1402c16d0f708b6111257a4b","observation_id":"5c2346d6-5ebd-4928-9e37-058a742bf841","resolution":{"observed_at":"2026-08-12T12:13:41.867365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.657015Z","title":"Graph con- trastive learning with augmentations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.657015Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:dc42b5b2d6dd57ef0a7d8af406a0b5fd210472203d98c1b1bc13b4e4f8eae29e","observation_id":"3a601165-89bc-41da-970f-ed4409d1bfa5","resolution":{"observed_at":"2026-08-12T12:13:41.657015Z","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":"2026-08-12T12:13:41.660457Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.660457Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:ffab3da959e8f5eb350e8b07b668cd777ff8bd803445e1805fc95f5fc8731e10","observation_id":"693555ee-2f6b-4348-87b8-478d78f7bae4","resolution":{"observed_at":"2026-08-12T12:13:41.660457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-08-16T18:14:15.699285Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-12T12:13:41.664166Z","title":"Time-series representation learning via temporal and contextual con- trasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.664166Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:89b0dc923afb7a7875fc63a2229f37cf3d1b9c793259d0453ff24f6e79922eef","observation_id":"d6920fd9-d66a-4748-be98-fd487e150383","resolution":{"observed_at":"2026-08-12T12:13:41.664166Z","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":"2026-08-12T12:13:41.668451Z","title":"Timesurl: Self-supervised contrastive learning for universal time series representation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.668451Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:4baf3b29dc398d516d8d51eeeae59af53c0621d99a8d40bb7e7bc20e62f36ef1","observation_id":"6372afc5-d9a0-40da-908f-e6258661dcfe","resolution":{"observed_at":"2026-08-12T12:13:41.668451Z","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":"2026-08-12T12:13:41.829894Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis,","venue":null,"work_id":"31562a23-5a91-4954-ad30-61925f5cdd70","year":2023},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.672069Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:af2210a8f276874b61ce9247f2ca184e2dae92038eb009a46b58d20a61987fd1","observation_id":"fab51539-b4f0-4f24-a4cb-ba7e98224531","resolution":{"observed_at":"2026-08-12T12:13:41.834671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:13:41.814494Z","title":"Periodicity decoupling framework for long-term series forecasting,","venue":null,"work_id":"f3d91688-71f6-4c1c-b818-f26607e8bce7","year":2024},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.675651Z"},"links":{"citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:dce0d195e46271c9f2cfd2f02154958f99557a9ccacc8cc0eabf85d7527e3c17","observation_id":"ea397c29-f6cd-4527-8d9f-aa4d26dd5c25","resolution":{"observed_at":"2026-08-12T12:13:41.820464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08472","last_updated":"2024-05-06T04:00:17Z","snapshot_observed_at":"2026-08-16T14:01:25.533007Z","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":"2026-08-12T12:13:41.678751Z","title":"Tslanet: Rethinking transformers for time series representation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.678751Z"},"links":{"cited_paper":"/paper/2404.08472","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:3394af317ace303740565c42f15696f28b5f287b3371243dbef04954073f68ff","observation_id":"1c5fa8a6-ddd1-4c4a-8fe1-1f1cf0c879dc","resolution":{"observed_at":"2026-08-12T12:13:41.678751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-08-16T18:20:27.415469Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-12T12:13:41.681792Z","title":"Unsupervised representa- tion learning for time series with temporal neighborhood coding,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.681792Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:c05c46188aef1b937141a9b7393da186681e4d211380d8edebd373bf19bdc7c9","observation_id":"f533f8d3-7c75-4929-aab9-f3935fdd4684","resolution":{"observed_at":"2026-08-12T12:13:41.681792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-08-13T10:37:24.864456Z","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":"2026-08-12T12:13:41.685793Z","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.685793Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:e8aa1a650f7ef5f7698b09082357cc31bf7ae45de0ec48cea8f132c4e1405d49","observation_id":"ef3e3de4-0397-4b5f-b520-bc1f62717998","resolution":{"observed_at":"2026-08-12T12:13:41.685793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10434","last_updated":"2024-02-16T03:51:14Z","snapshot_observed_at":"2026-08-16T14:18:06.874881Z","submitted_at":"2024-02-16T03:51:14Z","title":"Parametric Augmentation for Time Series Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10434","snapshot_observed_at":"2026-08-12T12:13:41.689634Z","title":"Parametric augmentation for time series contrastive learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:41.689634Z"},"links":{"cited_paper":"/paper/2402.10434","citing_paper":"/paper/2411.17382"},"observation_digest":"sha256:9b1f7230ed77809dc37ba19b7eb734aaa58e92d7507753667812ea0b5c3ab5a5","observation_id":"2e98aa04-d199-417c-a451-5e78de6dcadd","resolution":{"observed_at":"2026-08-12T12:13:41.689634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.17382","last_updated":"2024-11-26T12:41:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T00:29:05.361128Z","submitted_at":"2024-11-26T12:41:42Z","title":"MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":36},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.17382."}