{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3TD77T5OCFZRQHUQH2JV5HA27M","short_pith_number":"pith:3TD77T5O","canonical_record":{"source":{"id":"1910.09620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-21T19:28:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d614b3ae1d52666898a7fa70d8faaa69d74747106f379dfd9b938e7e050848e4","abstract_canon_sha256":"c857ff6207dfb82787d03107d4fc6ef189fa3ce682c5b140ea03f0a759cf6989"},"schema_version":"1.0"},"canonical_sha256":"dcc7ffcfae1173181e903e935e9c1afb0d4c5a61d552748da608cf44d9cbe8bc","source":{"kind":"arxiv","id":"1910.09620","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09620","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09620v1","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09620","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"3TD77T5OCFZR","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"3TD77T5OCFZRQHUQ","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"3TD77T5O","created_at":"2026-07-05T00:14:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3TD77T5OCFZRQHUQH2JV5HA27M","target":"record","payload":{"canonical_record":{"source":{"id":"1910.09620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-21T19:28:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d614b3ae1d52666898a7fa70d8faaa69d74747106f379dfd9b938e7e050848e4","abstract_canon_sha256":"c857ff6207dfb82787d03107d4fc6ef189fa3ce682c5b140ea03f0a759cf6989"},"schema_version":"1.0"},"canonical_sha256":"dcc7ffcfae1173181e903e935e9c1afb0d4c5a61d552748da608cf44d9cbe8bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:14:00.907171Z","signature_b64":"33r+Z7TqaALXyWpn8M11QYhyEddJZb7tSaWjjJJyKya03+UVv07k4mYKPme90LQomo9VIxBRMvcf1lMTgYmTBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcc7ffcfae1173181e903e935e9c1afb0d4c5a61d552748da608cf44d9cbe8bc","last_reissued_at":"2026-07-05T00:14:00.906782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:14:00.906782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.09620","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W0O0rOOeu8jEqJY+Si9B10QTI1JHBre5ZsjPXnzb8NxJYskLP5MhDZ5eRaJMGkMKWxcBsifaSv4+YCtFWzCrCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:59:09.596568Z"},"content_sha256":"c35ab21292131fd9527e45175c300b22b6ced9312f451c1112372dec579946e5","schema_version":"1.0","event_id":"sha256:c35ab21292131fd9527e45175c300b22b6ced9312f451c1112372dec579946e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3TD77T5OCFZRQHUQH2JV5HA27M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"You May Not Need Order in Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Qiao Jiang, Shurui Li, Xiaoyong Jin, Xifeng Yan, Xueying Ma, Yunkai Zhang","submitted_at":"2019-10-21T19:28:24Z","abstract_excerpt":"Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies am"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09620","kind":"arxiv","version":1},"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/1910.09620/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":"2026-07-05T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MuFeXJSUVmeseexljONx3G1vKNscinPt3T0gqZMqHU14jsEpHeA3TsVaut2hbmO1eA78kXqBgkHTrDn8C0xcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:59:09.597295Z"},"content_sha256":"63107b03fd0aff5bc7d404eb0c706553aa6d1e4d98d7f3d87c9be81c54a5232a","schema_version":"1.0","event_id":"sha256:63107b03fd0aff5bc7d404eb0c706553aa6d1e4d98d7f3d87c9be81c54a5232a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3TD77T5OCFZRQHUQH2JV5HA27M/bundle.json","state_url":"https://pith.science/pith/3TD77T5OCFZRQHUQH2JV5HA27M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3TD77T5OCFZRQHUQH2JV5HA27M/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":"2026-08-06T20:59:09Z","links":{"resolver":"https://pith.science/pith/3TD77T5OCFZRQHUQH2JV5HA27M","bundle":"https://pith.science/pith/3TD77T5OCFZRQHUQH2JV5HA27M/bundle.json","state":"https://pith.science/pith/3TD77T5OCFZRQHUQH2JV5HA27M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3TD77T5OCFZRQHUQH2JV5HA27M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3TD77T5OCFZRQHUQH2JV5HA27M","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":"c857ff6207dfb82787d03107d4fc6ef189fa3ce682c5b140ea03f0a759cf6989","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-21T19:28:24Z","title_canon_sha256":"d614b3ae1d52666898a7fa70d8faaa69d74747106f379dfd9b938e7e050848e4"},"schema_version":"1.0","source":{"id":"1910.09620","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09620","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09620v1","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09620","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"3TD77T5OCFZR","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"3TD77T5OCFZRQHUQ","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"3TD77T5O","created_at":"2026-07-05T00:14:00Z"}],"graph_snapshots":[{"event_id":"sha256:63107b03fd0aff5bc7d404eb0c706553aa6d1e4d98d7f3d87c9be81c54a5232a","target":"graph","created_at":"2026-07-05T00:14:00Z","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/1910.09620/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies am","authors_text":"Qiao Jiang, Shurui Li, Xiaoyong Jin, Xifeng Yan, Xueying Ma, Yunkai Zhang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-21T19:28:24Z","title":"You May Not Need Order in Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09620","kind":"arxiv","version":1},"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:c35ab21292131fd9527e45175c300b22b6ced9312f451c1112372dec579946e5","target":"record","created_at":"2026-07-05T00:14:00Z","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":"c857ff6207dfb82787d03107d4fc6ef189fa3ce682c5b140ea03f0a759cf6989","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-21T19:28:24Z","title_canon_sha256":"d614b3ae1d52666898a7fa70d8faaa69d74747106f379dfd9b938e7e050848e4"},"schema_version":"1.0","source":{"id":"1910.09620","kind":"arxiv","version":1}},"canonical_sha256":"dcc7ffcfae1173181e903e935e9c1afb0d4c5a61d552748da608cf44d9cbe8bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcc7ffcfae1173181e903e935e9c1afb0d4c5a61d552748da608cf44d9cbe8bc","first_computed_at":"2026-07-05T00:14:00.906782Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:14:00.906782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"33r+Z7TqaALXyWpn8M11QYhyEddJZb7tSaWjjJJyKya03+UVv07k4mYKPme90LQomo9VIxBRMvcf1lMTgYmTBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:14:00.907171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.09620","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c35ab21292131fd9527e45175c300b22b6ced9312f451c1112372dec579946e5","sha256:63107b03fd0aff5bc7d404eb0c706553aa6d1e4d98d7f3d87c9be81c54a5232a"],"state_sha256":"156a9d365136c21392c2f15c73590a9d35300a6d3e98c76f74c500861f588329"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mCKzSUfIhLDeWsFhz1VGNGMNMvFVnFJOc+Uzsk1bc//2qQLyCo8U7OXp6XIDEvE/vcwyCUFW5Qr4deUCaACQBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:59:09.603446Z","bundle_sha256":"5eda4d2aad45fe4836df1428aeb42246f2e6d871b36010c91eca13a4713ec306"}}