{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KKKHDYCKLFH4SHQI2CJARKJOTH","short_pith_number":"pith:KKKHDYCK","canonical_record":{"source":{"id":"2205.13504","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-05-26T17:17:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d1e91df11fe753faec6f172507abc922033732ef882ab823eae4cdec0238ff8b","abstract_canon_sha256":"596b3bd099f147e8cfe5528d4c9437f1dc6b70a7c249cea892a77d4a163c8203"},"schema_version":"1.0"},"canonical_sha256":"529471e04a594fc91e08d09208a92e99d1d54a96fcd5208cb660b1d6eaf66592","source":{"kind":"arxiv","id":"2205.13504","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.13504","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"arxiv_version","alias_value":"2205.13504v3","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.13504","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_12","alias_value":"KKKHDYCKLFH4","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_16","alias_value":"KKKHDYCKLFH4SHQI","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_8","alias_value":"KKKHDYCK","created_at":"2026-07-05T04:49:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KKKHDYCKLFH4SHQI2CJARKJOTH","target":"record","payload":{"canonical_record":{"source":{"id":"2205.13504","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-05-26T17:17:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d1e91df11fe753faec6f172507abc922033732ef882ab823eae4cdec0238ff8b","abstract_canon_sha256":"596b3bd099f147e8cfe5528d4c9437f1dc6b70a7c249cea892a77d4a163c8203"},"schema_version":"1.0"},"canonical_sha256":"529471e04a594fc91e08d09208a92e99d1d54a96fcd5208cb660b1d6eaf66592","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:23.126183Z","signature_b64":"xor4Jq7ahn+iM9CO4eF7pC8WLgTLdZZ5KPBXm9Ie/bU/pIfAnitZb8XIhwl/RjzPaUr03D28oqZGZUUswV8FAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"529471e04a594fc91e08d09208a92e99d1d54a96fcd5208cb660b1d6eaf66592","last_reissued_at":"2026-07-05T04:49:23.125629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:23.125629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.13504","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:49:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AhLiVXRsgSu19e4+dbZkKWr1YOdthI73nOAKk2sHWqJr2AusT829Do7qS8Z4zH4KebloIjrKReu3htdLpnAfBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T10:52:03.927049Z"},"content_sha256":"115eb1bffa1e574dcb862872b8b67815496112e1d10859fb1cb3afdc3fc72dd8","schema_version":"1.0","event_id":"sha256:115eb1bffa1e574dcb862872b8b67815496112e1d10859fb1cb3afdc3fc72dd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KKKHDYCKLFH4SHQI2CJARKJOTH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Are Transformers Effective for Time Series Forecasting?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Ailing Zeng, Lei Zhang, Muxi Chen, Qiang Xu","submitted_at":"2022-05-26T17:17:08Z","abstract_excerpt":"Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.13504","kind":"arxiv","version":3},"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/2205.13504/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":"2026-07-05T04:49:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zKzicIS3OSXpK258kOMUORcScSSHhFeLNLbmPNd/mNEV16qZn+58ct56Cl7ZYiMfpBvdoE87I6hSlx7baDDpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T10:52:03.927439Z"},"content_sha256":"4b20bc82be9ec86348bd991311dd8ee45eeaf3464121df64ff45e9d50bd00af4","schema_version":"1.0","event_id":"sha256:4b20bc82be9ec86348bd991311dd8ee45eeaf3464121df64ff45e9d50bd00af4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/bundle.json","state_url":"https://pith.science/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/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":"2026-07-28T10:52:03Z","links":{"resolver":"https://pith.science/pith/KKKHDYCKLFH4SHQI2CJARKJOTH","bundle":"https://pith.science/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/bundle.json","state":"https://pith.science/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KKKHDYCKLFH4SHQI2CJARKJOTH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KKKHDYCKLFH4SHQI2CJARKJOTH","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":"596b3bd099f147e8cfe5528d4c9437f1dc6b70a7c249cea892a77d4a163c8203","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-05-26T17:17:08Z","title_canon_sha256":"d1e91df11fe753faec6f172507abc922033732ef882ab823eae4cdec0238ff8b"},"schema_version":"1.0","source":{"id":"2205.13504","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.13504","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"arxiv_version","alias_value":"2205.13504v3","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.13504","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_12","alias_value":"KKKHDYCKLFH4","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_16","alias_value":"KKKHDYCKLFH4SHQI","created_at":"2026-07-05T04:49:23Z"},{"alias_kind":"pith_short_8","alias_value":"KKKHDYCK","created_at":"2026-07-05T04:49:23Z"}],"graph_snapshots":[{"event_id":"sha256:4b20bc82be9ec86348bd991311dd8ee45eeaf3464121df64ff45e9d50bd00af4","target":"graph","created_at":"2026-07-05T04:49:23Z","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/2205.13504/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate p","authors_text":"Ailing Zeng, Lei Zhang, Muxi Chen, Qiang Xu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-05-26T17:17:08Z","title":"Are Transformers Effective for Time Series Forecasting?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.13504","kind":"arxiv","version":3},"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":"sha256:115eb1bffa1e574dcb862872b8b67815496112e1d10859fb1cb3afdc3fc72dd8","target":"record","created_at":"2026-07-05T04:49:23Z","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":"596b3bd099f147e8cfe5528d4c9437f1dc6b70a7c249cea892a77d4a163c8203","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-05-26T17:17:08Z","title_canon_sha256":"d1e91df11fe753faec6f172507abc922033732ef882ab823eae4cdec0238ff8b"},"schema_version":"1.0","source":{"id":"2205.13504","kind":"arxiv","version":3}},"canonical_sha256":"529471e04a594fc91e08d09208a92e99d1d54a96fcd5208cb660b1d6eaf66592","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"529471e04a594fc91e08d09208a92e99d1d54a96fcd5208cb660b1d6eaf66592","first_computed_at":"2026-07-05T04:49:23.125629Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:23.125629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xor4Jq7ahn+iM9CO4eF7pC8WLgTLdZZ5KPBXm9Ie/bU/pIfAnitZb8XIhwl/RjzPaUr03D28oqZGZUUswV8FAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:23.126183Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.13504","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:115eb1bffa1e574dcb862872b8b67815496112e1d10859fb1cb3afdc3fc72dd8","sha256:4b20bc82be9ec86348bd991311dd8ee45eeaf3464121df64ff45e9d50bd00af4"],"state_sha256":"63aef824c0b6999f5e5aa78c9c465242ed8e2c3d33e76550a00bd7d7ac35ba6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1uZSKSGfLcYId3XDEq73J5BpOJJ+63gAC308s1Vw6gFgpt8yT3lVLl+yysMYI0YjbcOEpjoiueobpBbi4bKjAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-28T10:52:03.929903Z","bundle_sha256":"b5820406b3f2cc698387b97f2958226fb7cefcde2111b2598f89f9602a2928f7"}}