{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GPS7PZGALMTMQBSASQZEMRRCU2","short_pith_number":"pith:GPS7PZGA","canonical_record":{"source":{"id":"2302.11939","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T11:37:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e5246db2f1fe038382801af4788bdf8d8c49ce02f140b99b4b0148e8cb5a3202","abstract_canon_sha256":"159762b912cd7f4e4b3dddf6e6c3dc9c3833f2519c1469b7c380bf207e020e01"},"schema_version":"1.0"},"canonical_sha256":"33e5f7e4c05b26c806409432464622a6a9656cabfd246e3db76b79b8d152899b","source":{"kind":"arxiv","id":"2302.11939","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11939","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11939v6","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11939","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_12","alias_value":"GPS7PZGALMTM","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_16","alias_value":"GPS7PZGALMTMQBSA","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_8","alias_value":"GPS7PZGA","created_at":"2026-07-05T07:00:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GPS7PZGALMTMQBSASQZEMRRCU2","target":"record","payload":{"canonical_record":{"source":{"id":"2302.11939","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T11:37:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e5246db2f1fe038382801af4788bdf8d8c49ce02f140b99b4b0148e8cb5a3202","abstract_canon_sha256":"159762b912cd7f4e4b3dddf6e6c3dc9c3833f2519c1469b7c380bf207e020e01"},"schema_version":"1.0"},"canonical_sha256":"33e5f7e4c05b26c806409432464622a6a9656cabfd246e3db76b79b8d152899b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:59.083328Z","signature_b64":"AWAKe0cWV7NEbvpah3Yhpk4GmNF+0+mVQXJ/t3f61qbWdakaNOs/rY9R7IuFt8UeeigiArMPERo4kHY2HsBYBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33e5f7e4c05b26c806409432464622a6a9656cabfd246e3db76b79b8d152899b","last_reissued_at":"2026-07-05T07:00:59.082878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:59.082878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.11939","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:00:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PKPn/fFDobxFuSRkDbXzjnud8IL4kuxbSYjxYNUdAHNGN2hwrlANjKmyMfcO1rlRQO3qWMqbJNpU7zCy+cnPBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:30:32.118641Z"},"content_sha256":"da43d298e25452051aa2a85589c48b426300d0a065f6f7cb51ecebe234f64313","schema_version":"1.0","event_id":"sha256:da43d298e25452051aa2a85589c48b426300d0a065f6f7cb51ecebe234f64313"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GPS7PZGALMTMQBSASQZEMRRCU2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"One Fits All:Power General Time Series Analysis by Pretrained LM","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Liang Sun, Peisong Niu, Rong Jin, Tian Zhou, Xue Wang","submitted_at":"2023-02-23T11:37:39Z","abstract_excerpt":"Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11939","kind":"arxiv","version":6},"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/2302.11939/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":"2026-07-05T07:00:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J7079bhfxYINqwkGAb82hRYvKd1ts53O6zyDY66EF4aiyGYeh+f6R0So9rj6hs0U6TcH203BKBrIWIZElzRmAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:30:32.119444Z"},"content_sha256":"e34979988aaf199abd7b4a16c76f746e8421a6a6590e2967159bfdeaecf5701c","schema_version":"1.0","event_id":"sha256:e34979988aaf199abd7b4a16c76f746e8421a6a6590e2967159bfdeaecf5701c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GPS7PZGALMTMQBSASQZEMRRCU2/bundle.json","state_url":"https://pith.science/pith/GPS7PZGALMTMQBSASQZEMRRCU2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GPS7PZGALMTMQBSASQZEMRRCU2/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":"2026-08-11T06:30:32Z","links":{"resolver":"https://pith.science/pith/GPS7PZGALMTMQBSASQZEMRRCU2","bundle":"https://pith.science/pith/GPS7PZGALMTMQBSASQZEMRRCU2/bundle.json","state":"https://pith.science/pith/GPS7PZGALMTMQBSASQZEMRRCU2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GPS7PZGALMTMQBSASQZEMRRCU2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GPS7PZGALMTMQBSASQZEMRRCU2","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":"159762b912cd7f4e4b3dddf6e6c3dc9c3833f2519c1469b7c380bf207e020e01","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T11:37:39Z","title_canon_sha256":"e5246db2f1fe038382801af4788bdf8d8c49ce02f140b99b4b0148e8cb5a3202"},"schema_version":"1.0","source":{"id":"2302.11939","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11939","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11939v6","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11939","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_12","alias_value":"GPS7PZGALMTM","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_16","alias_value":"GPS7PZGALMTMQBSA","created_at":"2026-07-05T07:00:59Z"},{"alias_kind":"pith_short_8","alias_value":"GPS7PZGA","created_at":"2026-07-05T07:00:59Z"}],"graph_snapshots":[{"event_id":"sha256:e34979988aaf199abd7b4a16c76f746e8421a6a6590e2967159bfdeaecf5701c","target":"graph","created_at":"2026-07-05T07:00:59Z","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/2302.11939/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address th","authors_text":"Liang Sun, Peisong Niu, Rong Jin, Tian Zhou, Xue Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T11:37:39Z","title":"One Fits All:Power General Time Series Analysis by Pretrained LM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11939","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:da43d298e25452051aa2a85589c48b426300d0a065f6f7cb51ecebe234f64313","target":"record","created_at":"2026-07-05T07:00:59Z","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":"159762b912cd7f4e4b3dddf6e6c3dc9c3833f2519c1469b7c380bf207e020e01","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T11:37:39Z","title_canon_sha256":"e5246db2f1fe038382801af4788bdf8d8c49ce02f140b99b4b0148e8cb5a3202"},"schema_version":"1.0","source":{"id":"2302.11939","kind":"arxiv","version":6}},"canonical_sha256":"33e5f7e4c05b26c806409432464622a6a9656cabfd246e3db76b79b8d152899b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33e5f7e4c05b26c806409432464622a6a9656cabfd246e3db76b79b8d152899b","first_computed_at":"2026-07-05T07:00:59.082878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:59.082878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AWAKe0cWV7NEbvpah3Yhpk4GmNF+0+mVQXJ/t3f61qbWdakaNOs/rY9R7IuFt8UeeigiArMPERo4kHY2HsBYBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:59.083328Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.11939","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da43d298e25452051aa2a85589c48b426300d0a065f6f7cb51ecebe234f64313","sha256:e34979988aaf199abd7b4a16c76f746e8421a6a6590e2967159bfdeaecf5701c"],"state_sha256":"140ee50a479930b9a7b38211496962318273a7fd3f20e2c0481da902f6c45896"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"01d6JuFW+y+oG8Pk22mkMUkansfB5uiwBSFww5ZzBBO9P0M2oNFMJUUrE1eRnPQwU+wpescrOucBKQl8iwwACA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T06:30:32.124376Z","bundle_sha256":"47c1e0597015c369f4354fc77a9f23ab421d44c7a141663f950ee69931b25ee2"}}