{"as_of":"2026-08-13T08:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12595ad16fcf214cebcf848c67af09fd706cef433f797003aedca807bd66c126","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:32:03.813848Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2501.01394/citation-record","integrity":"/paper/2501.01394/integrity","json":"/paper/2501.01394/citation-record.json","paper":"/paper/2501.01394"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:05.600363Z","title":null,"venue":null,"work_id":"92dda4dd-4936-4c29-b56a-5f44228dfa5c","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.404720Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:bfe57cc89bd8d7d04f443b32991621bdc0753375901f1c08b68369d125b88e6a","observation_id":"206ab47d-b10d-4cf8-a2d0-04b989b3f8fe","resolution":{"observed_at":"2026-08-10T22:32:05.613546Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.571222Z","title":"Transformers in time-series anal- ysis: A tutorial","venue":null,"work_id":"6c490797-d64f-4abe-a18e-2b31b5e70d47","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.414449Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:28236e0e604f4b0d898385e1146b1415a9e2e05c403264e833dff2655ddd1d28","observation_id":"5fe46ffc-b8a3-43f0-a450-0dd98cbb0297","resolution":{"observed_at":"2026-08-10T22:32:05.579691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.532911Z","title":"Neural architecture search benchmarks: Insights and survey","venue":null,"work_id":"b851851b-dab4-4c9d-83ef-6b88d2bf4509","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.439509Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:6c578cdb1e37af02228878dbb338b6bf29a46edfd63b9ff22508e6d806d2e2aa","observation_id":"2fdeb33e-7db2-4c2d-88bc-ae00f2125d8e","resolution":{"observed_at":"2026-08-10T22:32:05.546438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.499391Z","title":"Neural architecture search for transformers: A survey","venue":null,"work_id":"8ff1a16a-ec08-4e6f-b57e-6133956c2214","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.452689Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:4d66bfc79d10352ce0c0bfe17c8f66eddd23a19aa803303917d58678a292171c","observation_id":"d78fb256-1fc5-4852-ab71-410882b38243","resolution":{"observed_at":"2026-08-10T22:32:05.510310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1502.02127","last_updated":"2015-04-06T15:44:52Z","snapshot_observed_at":"2026-08-09T20:58:03.759780Z","submitted_at":"2015-02-07T11:46:22Z","title":"Hyperparameter Search in Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.02127","snapshot_observed_at":"2026-08-10T22:32:03.468786Z","title":"Hyperparame- ter search in machine learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.468786Z"},"links":{"cited_paper":"/paper/1502.02127","citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:32d95f6331f216e447c75a0fe9fbab9bc55dd8cf0d80420ba7c8f7bb2360285f","observation_id":"15e54d71-6db8-4464-915a-0c9010b2b6d3","resolution":{"observed_at":"2026-08-10T22:32:03.468786Z","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":"2026-08-10T22:32:05.457125Z","title":"25 years of time series forecasting","venue":null,"work_id":"1e3dab50-f04f-4a1e-8037-14bfe3ca45c4","year":2006},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.476662Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:f18b8ef35ffbbce42d582945b00cce5515d0d4febb9b644c857095e26b846d94","observation_id":"2aef185c-f0b0-412a-a6e5-861cd04e3cb1","resolution":{"observed_at":"2026-08-10T22:32:05.471907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.409784Z","title":"Algorithms for hyperparameter tuning of lstms for time series forecasting","venue":null,"work_id":"fb190d74-00b9-4703-b6d3-eed9377dbb0e","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.485316Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:f5e877cf609713552ddcf331f723578fd96fffacde7622b8f5296cd2e825b1dc","observation_id":"9bc38b88-b6db-4f81-b0e2-e311240a4554","resolution":{"observed_at":"2026-08-10T22:32:05.419684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.369297Z","title":"Hyperparameter op- timization","venue":null,"work_id":"018aa375-e1e0-4f14-9d9b-738eb22d0d61","year":2019},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.490750Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:1274b8c86af7bb708c29abdb12019ddf458bee84bacbd1cf5e7eae45bc8d1624","observation_id":"9cc8a5a5-1e0d-4c1d-ad51-ba95e210bec5","resolution":{"observed_at":"2026-08-10T22:32:05.376700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-10T22:32:03.511966Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.511966Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:81dd63bd1f1fec4fb694b4cf9a1306ea7881abd27878df2deccc6abeea744ed4","observation_id":"e9fd83eb-a647-4e7f-b64c-3f3a3b6a8a59","resolution":{"observed_at":"2026-08-10T22:32:03.511966Z","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":"2026-08-10T22:32:05.324131Z","title":"Advanced hyperparameter optimization of deep learning models for wind power prediction","venue":null,"work_id":"52c3fd93-73b5-4c6a-95ac-0ae2ea0b57a2","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.524837Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:b88600ff1b70fdbc893a1b45befe062a756cbd35ca08bf11f0ea2647bbc458da","observation_id":"6d2b6f95-a985-4744-9678-50ac59aeeb32","resolution":{"observed_at":"2026-08-10T22:32:05.340607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.255313Z","title":"Multivariate time series forecasting with dynamic graph neural odes","venue":null,"work_id":"44efc505-d923-4698-a2d1-8f0686bd1ff6","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.539382Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:775b24b41c803d37f12c000356eb294ad596c4627dd30e4e26c3ac3786d4929c","observation_id":"6fc89ad9-e15e-4e32-87b9-a4c62f8ddc03","resolution":{"observed_at":"2026-08-10T22:32:05.279367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.198595Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","venue":null,"work_id":"50819976-79bc-4bb6-9101-3b6500b029d6","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.552315Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:a3bde1f754379f882dd949e724f8585566a118dfcb36e8dfabc0d4a7ffdffa1c","observation_id":"9727a983-eb9f-4c34-bf9b-c35cf88abb96","resolution":{"observed_at":"2026-08-10T22:32:05.208653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.169221Z","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","venue":null,"work_id":"660f5b93-7697-4bb7-b1e8-ba9e5be9101c","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.559936Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:b3b03fc3349c829c27a48d5b98e9f21fdbcaf5cc4c39d2227b0712472ace5453","observation_id":"e9b56602-a281-400b-b962-66e27a5ea4fb","resolution":{"observed_at":"2026-08-10T22:32:05.177257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.106880Z","title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","venue":null,"work_id":"4403e1f7-3830-4c12-9979-535858794987","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.566927Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:30f0ced4297cb41c2ec19c2398b3a0471e8a0134ab90d4e7969646ef83bb181d","observation_id":"5d84c628-3a22-4024-a9dd-e54bd2aec329","resolution":{"observed_at":"2026-08-10T22:32:05.123163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:05.044161Z","title":"Review of automated time series forecasting pipelines","venue":null,"work_id":"6e523744-d37a-44db-ab48-90e50a1e623f","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.574633Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:d7dda23342b09b9e328f5ff2055d996d25d281faa26887e960c62046bf61c6e5","observation_id":"a83d81b9-1d72-42bf-9537-c445f59285d4","resolution":{"observed_at":"2026-08-10T22:32:05.058778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.987153Z","title":"A Time Series is Worth 64 Words: Long- term Forecasting with Transformers","venue":null,"work_id":"c862a262-43e7-4dac-bcc0-e5420d854c88","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.582000Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:a1a86bdb8ce3a505bc24e2d1d2f43e767a858ac424bd0c6a3f3ae204b585b9a1","observation_id":"6cf8cd36-34b9-444c-81e6-f5b6f1ba7f24","resolution":{"observed_at":"2026-08-10T22:32:04.998648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.940624Z","title":"Forecasting: theory and prac- tice","venue":null,"work_id":"8e2efa61-61ea-4954-a6ae-28b521d7e38d","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.588710Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:506911d85588ba8a57f749e571330239d1dbcdebdc54e4396af5f9ee600c1576","observation_id":"55fcba2f-5487-45dc-be58-943d0c8e0623","resolution":{"observed_at":"2026-08-10T22:32:04.954271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.00247","last_updated":"2018-05-02T15:27:25Z","snapshot_observed_at":"2026-08-02T09:10:42.204876Z","submitted_at":"2018-04-01T01:59:52Z","title":"Training Tips for the Transformer Model","version":2},"cited_work":{"arxiv_id":"1804.00247","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.00247","snapshot_observed_at":"2026-08-10T22:32:03.987642Z","title":"Training Tips for the Transformer Model","venue":"cs.CL","work_id":"f62491b3-eeec-42cf-a437-e71dbc35050a","year":2018},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.602833Z"},"links":{"cited_paper":"/paper/1804.00247","citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:ac74aca14f6b6a9468a6965b32271ecd7737dfe99a060d5169c597d34d69c54b","observation_id":"fea3a419-d1c4-4060-bcbd-100342f6ddf7","resolution":{"observed_at":"2026-08-10T22:32:03.995867Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.880777Z","title":"Autotransformer: Automatic trans- former architecture design for time series classification","venue":null,"work_id":"4d0c02c6-68f8-47ad-91f5-56091382de73","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.608448Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:6ce16ec27472277f5c6c9f63364e3c07d8934631da612d1f5f5f31e266973104","observation_id":"0c4bc863-a4d9-4c12-bdb6-63f82cd3fee7","resolution":{"observed_at":"2026-08-10T22:32:04.888098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.836255Z","title":"Distributed hyperparameter opti- mization based multivariate time series forecasting","venue":null,"work_id":"82c9adfe-d0c6-4eed-a442-6f144ea71da0","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.614946Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:d60e5c9312be9b50c862f10912868719c375119e0b109ed610208f0a8c536e6a","observation_id":"eaf248ce-00c7-4999-b2ec-c92f01f7b407","resolution":{"observed_at":"2026-08-10T22:32:04.855683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.769569Z","title":"Hypertuned temporal fusion transformer for multi-horizon time series fore- casting of dam level in hydroelectric power plants","venue":null,"work_id":"8c4149f6-523b-499c-a194-dc6ac237bc4f","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.628566Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:36485bff3ea28858b72071386ff961015c56c8abb7b185b39b1850549ab4842c","observation_id":"d313f216-d2ee-4ddf-96b0-432f20fc332e","resolution":{"observed_at":"2026-08-10T22:32:04.787179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.714534Z","title":"Optimization of deep neural networks: a survey and unified taxonomy","venue":null,"work_id":"5b006f3b-6007-401c-84a8-aca4394fafae","year":2020},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.649427Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:10061d3776037723f738db80577a0403f07914cda95d97fc70b79bb3e2fa8d3d","observation_id":"30fc6345-c6d3-49f3-bd3e-87b5bba162bf","resolution":{"observed_at":"2026-08-10T22:32:04.736699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.646667Z","title":"TimeMixer: Decomposable Multi- scale Mixing for Time Series Forecasting","venue":null,"work_id":"b5cd8908-0e66-4dfd-b3cf-5e6684a17f9f","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.663719Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:184df57e3bb2a7b7efccc49cec1ffca532ed804638f129a496d86ec081efc84d","observation_id":"4c17380c-3417-4c6d-a084-c89cfc86d4f7","resolution":{"observed_at":"2026-08-10T22:32:04.660506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.606102Z","title":"Time Series Data Augmentation for Deep Learning: A Survey","venue":null,"work_id":"f8d6a369-6922-4927-a21c-e38ef3dcb1e4","year":2021},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.684716Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:b180cc7984d2a87757d6ae7a0dee4f2cfb9ac3ee8bb9738c29f6a2c35be8d36e","observation_id":"fe6adb93-be45-41ad-b0e6-9575f4803813","resolution":{"observed_at":"2026-08-10T22:32:04.619342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.541069Z","title":"Transformers in time series: a survey","venue":null,"work_id":"bd4f1aac-d933-4994-9eb4-1329bb76b0cd","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.702079Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:722ab9fd2cf54ff6ebe00f000d0b1b185488dcdc871edd2886d55edea16f7679","observation_id":"132b44eb-c787-4579-8940-4dd218dd563f","resolution":{"observed_at":"2026-08-10T22:32:04.553860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.499427Z","title":"Strategic Predictions and Explana- tions By Machine Learning","venue":null,"work_id":"3cd48f17-e2e4-4a45-a779-3470766d1252","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.717752Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:bc2f24a9bd874b6b7f39488f43cf0ba130498bdd9c2f926e36fb7100eec64026","observation_id":"4ef2e03d-b8e3-4364-8373-27b786713305","resolution":{"observed_at":"2026-08-10T22:32:04.513465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.454645Z","title":"Trustworthy AI: Deciding What to Decide","venue":null,"work_id":"8598c7a5-5dea-45f2-a42e-124a76faa62c","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.732232Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:4b04173e9b60022509cd8bf11786354f20db8c23b5e75176426d7fd9e0617d03","observation_id":"2a139071-87cf-4baa-a15d-269adf5872d6","resolution":{"observed_at":"2026-08-10T22:32:04.470381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.363776Z","title":"Autoformer: decomposition transform- ers with auto-correlation for long-term series forecast- ing","venue":null,"work_id":"e193889e-5bc2-4656-a81e-b45f80b818d0","year":2021},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.746186Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:2417d2c02e603093fccd97a64e96de56c7fd8ee18e054eaf978881b9e1943ceb","observation_id":"3e116db1-0671-4a03-bcd4-849c0b77b248","resolution":{"observed_at":"2026-08-10T22:32:04.410208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.301462Z","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","venue":null,"work_id":"a8dc4049-7b43-4021-801a-38cbd6ad36da","year":2023},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.758047Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:2177f2b58c2f399d3f222251c723a78038221a00b4293919aedcd81335ab2b67","observation_id":"b8bdf95d-f968-432f-8932-0e952b811ead","resolution":{"observed_at":"2026-08-10T22:32:04.306828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19784","last_updated":"2024-07-29T08:27:21Z","snapshot_observed_at":"2026-08-12T23:13:05.390099Z","submitted_at":"2024-07-29T08:27:21Z","title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.19784","snapshot_observed_at":"2026-08-10T22:32:03.777499Z","title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Fore- casting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.777499Z"},"links":{"cited_paper":"/paper/2407.19784","citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:bdda5258e21e8e4f0f51b4d9bb6026d0449defc0e22924126056e28d2f2cecce","observation_id":"2bad43b3-74a1-4e3c-a694-c9ef81c3c17f","resolution":{"observed_at":"2026-08-10T22:32:03.777499Z","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":"2026-08-10T22:32:04.270419Z","title":"Transformer Multivariate Forecasting: Less is More?","venue":null,"work_id":"c41ecaa2-eb71-4150-a8a5-d3c8d4eb1aa0","year":2024},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.788306Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:6157aeded63383d7d6732b1296c71da3e64e7dd8136a42116d47691e7d8ee160","observation_id":"dfb505e8-eb14-48b4-8f77-3e0ee2aa1423","resolution":{"observed_at":"2026-08-10T22:32:04.285051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.230534Z","title":"On hyperparameter op- timization of machine learning algorithms: Theory and practice","venue":null,"work_id":"b32a3474-8816-44b3-970d-36dbf7e54c45","year":2020},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.795599Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:a2eb69abccab9193f491108b7dc65289449e91205065280972426647eafc3b1f","observation_id":"b17b8d64-204d-4e82-af80-c897060be7db","resolution":{"observed_at":"2026-08-10T22:32:04.246849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05689","last_updated":"2020-03-12T10:12:22Z","snapshot_observed_at":"2026-08-11T04:06:19.914166Z","submitted_at":"2020-03-12T10:12:22Z","title":"Hyper-Parameter Optimization: A Review of Algorithms and Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05689","snapshot_observed_at":"2026-08-10T22:32:03.801595Z","title":"Hyper-parameter optimization: A review of algorithms and applications","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.801595Z"},"links":{"cited_paper":"/paper/2003.05689","citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:5c77b6400fdf3b9e483074eb3db809509a7729833993a857e397d68955248bdd","observation_id":"3ef7e9a2-8724-4b15-9e01-0898494bfc2e","resolution":{"observed_at":"2026-08-10T22:32:03.801595Z","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":"2026-08-10T22:32:04.179721Z","title":"Crossformer: Trans- former utilizing cross-dimension dependency for multi- variate time series forecasting","venue":null,"work_id":"04b48f93-2662-4c5a-b7a6-6922562d067b","year":2022},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.807636Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:0e55c7451d3f0f24369d20f046d43ef6bb7f8b548d46baef39d8f8960df8aaef","observation_id":"4493a413-70f3-4217-aabb-94d7ca9efed3","resolution":{"observed_at":"2026-08-10T22:32:04.195773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T22:32:04.119444Z","title":"Informer: Beyond efficient trans- former for long sequence time-series forecasting","venue":null,"work_id":"839d9366-731e-4671-aa06-7128ae4417a7","year":2021},"citing_paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:03.813848Z"},"links":{"citing_paper":"/paper/2501.01394"},"observation_digest":"sha256:b4822c65f5291273cc312a5f69fe18ce53aefeb576c58e9af3d3de47381bb84a","observation_id":"bada0239-b6ff-4b9f-a772-b6653f6b5a74","resolution":{"observed_at":"2026-08-10T22:32:04.132426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01394","last_updated":"2025-01-02T18:12:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T00:47:22.211580Z","submitted_at":"2025-01-02T18:12:42Z","title":"A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":29},"total_outbound_references":35},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.01394."}