{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NROWLMCVXQNYMANSZLSNLPGZFA","short_pith_number":"pith:NROWLMCV","canonical_record":{"source":{"id":"2205.08897","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0b83056e40b87ea2d029a9a39dde6f4415779dc07cdbfd2586a28d92071d643f","abstract_canon_sha256":"a83f1616ec68c3a20b019ae039cab828a9f4efc9283ccf066a1fe1bed2a85cce"},"schema_version":"1.0"},"canonical_sha256":"6c5d65b055bc1b8601b2cae4d5bcd92812203c5f40fd24b19879af649920133f","source":{"kind":"arxiv","id":"2205.08897","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08897","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08897v4","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08897","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_12","alias_value":"NROWLMCVXQNY","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_16","alias_value":"NROWLMCVXQNYMANS","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_8","alias_value":"NROWLMCV","created_at":"2026-07-05T04:58:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NROWLMCVXQNYMANSZLSNLPGZFA","target":"record","payload":{"canonical_record":{"source":{"id":"2205.08897","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0b83056e40b87ea2d029a9a39dde6f4415779dc07cdbfd2586a28d92071d643f","abstract_canon_sha256":"a83f1616ec68c3a20b019ae039cab828a9f4efc9283ccf066a1fe1bed2a85cce"},"schema_version":"1.0"},"canonical_sha256":"6c5d65b055bc1b8601b2cae4d5bcd92812203c5f40fd24b19879af649920133f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:04.129431Z","signature_b64":"eh+f/4eZnIcC6JiWojlN3dkehvu9JvbHWxpwZAxB5xWSEPFh7SwX2NTjWe+q26T88ROuy5IJovnl9WW4n5bpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c5d65b055bc1b8601b2cae4d5bcd92812203c5f40fd24b19879af649920133f","last_reissued_at":"2026-07-05T04:58:04.128996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:04.128996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.08897","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6nzjmhg88g4aLZ1pHJoAyyySo5j/0Iv7qXyauaj3K2/dchIlrzi5xOjwTMYcIRtpl9EKQiemZfKAhjvqYc/bDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:11:14.699285Z"},"content_sha256":"e73e4d3a391c33b53c16758a94844d0d30ef32b10906a1d9c951e6eac5951fd2","schema_version":"1.0","event_id":"sha256:e73e4d3a391c33b53c16758a94844d0d30ef32b10906a1d9c951e6eac5951fd2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NROWLMCVXQNYMANSZLSNLPGZFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Liang Sun, Qingsong Wen, Rong Jin, Tao Yao, Tian Zhou, Wotao Yin, Xue Wang, Ziqing Ma","submitted_at":"2022-05-18T12:37:54Z","abstract_excerpt":"Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoiding overfitting to noise presented in the history. Addressing this allows better utilization of the capabilities of deep learning models. To this end, we design a \\textbf{F}requency \\textbf{i}mproved \\textbf{L}egendre \\textbf{M}emory model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08897","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.08897/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X3IoVvFTUt+DKMtaqVVqFl04lYL5FI0kK85rODXvJqDn7bZ3enzSqjPMMQzslyLSi3ElB+p5OFTRU+54vYJvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:11:14.699667Z"},"content_sha256":"69caad8f502bc1309b4d4860b799f50d2dc4303e12b228d68b0a2f6c00d4ef67","schema_version":"1.0","event_id":"sha256:69caad8f502bc1309b4d4860b799f50d2dc4303e12b228d68b0a2f6c00d4ef67"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NROWLMCVXQNYMANSZLSNLPGZFA/bundle.json","state_url":"https://pith.science/pith/NROWLMCVXQNYMANSZLSNLPGZFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NROWLMCVXQNYMANSZLSNLPGZFA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T18:11:14Z","links":{"resolver":"https://pith.science/pith/NROWLMCVXQNYMANSZLSNLPGZFA","bundle":"https://pith.science/pith/NROWLMCVXQNYMANSZLSNLPGZFA/bundle.json","state":"https://pith.science/pith/NROWLMCVXQNYMANSZLSNLPGZFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NROWLMCVXQNYMANSZLSNLPGZFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NROWLMCVXQNYMANSZLSNLPGZFA","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":"a83f1616ec68c3a20b019ae039cab828a9f4efc9283ccf066a1fe1bed2a85cce","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:37:54Z","title_canon_sha256":"0b83056e40b87ea2d029a9a39dde6f4415779dc07cdbfd2586a28d92071d643f"},"schema_version":"1.0","source":{"id":"2205.08897","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08897","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08897v4","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08897","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_12","alias_value":"NROWLMCVXQNY","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_16","alias_value":"NROWLMCVXQNYMANS","created_at":"2026-07-05T04:58:04Z"},{"alias_kind":"pith_short_8","alias_value":"NROWLMCV","created_at":"2026-07-05T04:58:04Z"}],"graph_snapshots":[{"event_id":"sha256:69caad8f502bc1309b4d4860b799f50d2dc4303e12b228d68b0a2f6c00d4ef67","target":"graph","created_at":"2026-07-05T04:58:04Z","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/2205.08897/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoiding overfitting to noise presented in the history. Addressing this allows better utilization of the capabilities of deep learning models. To this end, we design a \\textbf{F}requency \\textbf{i}mproved \\textbf{L}egendre \\textbf{M}emory model","authors_text":"Liang Sun, Qingsong Wen, Rong Jin, Tao Yao, Tian Zhou, Wotao Yin, Xue Wang, Ziqing Ma","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:37:54Z","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08897","kind":"arxiv","version":4},"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:e73e4d3a391c33b53c16758a94844d0d30ef32b10906a1d9c951e6eac5951fd2","target":"record","created_at":"2026-07-05T04:58:04Z","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":"a83f1616ec68c3a20b019ae039cab828a9f4efc9283ccf066a1fe1bed2a85cce","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:37:54Z","title_canon_sha256":"0b83056e40b87ea2d029a9a39dde6f4415779dc07cdbfd2586a28d92071d643f"},"schema_version":"1.0","source":{"id":"2205.08897","kind":"arxiv","version":4}},"canonical_sha256":"6c5d65b055bc1b8601b2cae4d5bcd92812203c5f40fd24b19879af649920133f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c5d65b055bc1b8601b2cae4d5bcd92812203c5f40fd24b19879af649920133f","first_computed_at":"2026-07-05T04:58:04.128996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:58:04.128996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eh+f/4eZnIcC6JiWojlN3dkehvu9JvbHWxpwZAxB5xWSEPFh7SwX2NTjWe+q26T88ROuy5IJovnl9WW4n5bpCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:58:04.129431Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.08897","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e73e4d3a391c33b53c16758a94844d0d30ef32b10906a1d9c951e6eac5951fd2","sha256:69caad8f502bc1309b4d4860b799f50d2dc4303e12b228d68b0a2f6c00d4ef67"],"state_sha256":"6a6dcc79a9b46a8e6690ddcc0553ce2944037afe81126bae378cd815845e9896"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1p0IcXTDOizp/HYFSvTUvo50eADXJVzmQzpv1b4uyRuVCtvMc83/fMIBwAvLamufrrC1sCcBivKgCZR7DFhhDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T18:11:14.702173Z","bundle_sha256":"bbe234f3ab4612cc30b93e5e0bb0b3020654686523a5566f954bdad1b94be9c2"}}