{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TGCAHNZQ4OEZBZIQO2OHM2GXOG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"25ae68989705f87079ee42fdd958fc2648397357a00505a8b0f3b23511941b7b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T14:33:07Z","title_canon_sha256":"9e982e2fb39c949c1621beb6a613379161e636d02c818eb065f4a134b8a61516"},"schema_version":"1.0","source":{"id":"2302.02173","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.02173","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"arxiv_version","alias_value":"2302.02173v6","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02173","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_12","alias_value":"TGCAHNZQ4OEZ","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_16","alias_value":"TGCAHNZQ4OEZBZIQ","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_8","alias_value":"TGCAHNZQ","created_at":"2026-07-05T11:20:41Z"}],"graph_snapshots":[{"event_id":"sha256:cfef5e38f01d8e9537ebbe70da073f66c48438acb07256ef1cb104e88acf01e6","target":"graph","created_at":"2026-07-05T11:20:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2302.02173/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth ana","authors_text":"Guodong Long, Hui He, Hui Xiong, Kun Yi, Liang Hu, Longbing Cao, Qingsong Wen, Qi Zhang, Shoujin Wang, Wei Fan","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T14:33:07Z","title":"A Survey on Deep Learning based Time Series Analysis with Frequency Transformation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02173","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1f91c69b86b5ae2e974c59b585e4c994562ed9e72e823da2e2941213055c2349","target":"record","created_at":"2026-07-05T11:20:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"25ae68989705f87079ee42fdd958fc2648397357a00505a8b0f3b23511941b7b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T14:33:07Z","title_canon_sha256":"9e982e2fb39c949c1621beb6a613379161e636d02c818eb065f4a134b8a61516"},"schema_version":"1.0","source":{"id":"2302.02173","kind":"arxiv","version":6}},"canonical_sha256":"998403b730e38990e510769c7668d771ade9b077e18be12ee60c766278c762f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"998403b730e38990e510769c7668d771ade9b077e18be12ee60c766278c762f2","first_computed_at":"2026-07-05T11:20:41.976386Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:41.976386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BUUxfzK+cBaXuzGeoo48R02Cqo1rGgcTwd07iZva16JdKqWzy/nsC/lLvS/TpV539CdEoav/z//nDIRd2UxfDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:41.976866Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.02173","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f91c69b86b5ae2e974c59b585e4c994562ed9e72e823da2e2941213055c2349","sha256:cfef5e38f01d8e9537ebbe70da073f66c48438acb07256ef1cb104e88acf01e6"],"state_sha256":"f1d84faaa0664bfc15887a75fcb0b01e588ab98598ab9f7555200779506ec9ef"}