{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AOYVBU67DFNO5PHCSOBXZLYLNI","short_pith_number":"pith:AOYVBU67","schema_version":"1.0","canonical_sha256":"03b150d3df195aeebce293837caf0b6a3510690815bcd482ea14ed13295bf845","source":{"kind":"arxiv","id":"2606.13119","version":1},"attestation_state":"computed","paper":{"title":"MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Chongshou Li, Lilan Peng, Qingren Yao, Tianrui Li, Yandi Liu","submitted_at":"2026-06-11T09:48:18Z","abstract_excerpt":"Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio-temporal data exhibits temporal mirage: similar short-window inputs have divergent future trends, and vice versa. Existing spatio-temporal graph neural networks (STGNNs) cannot effectively identify such mirages. We argue that the core reason lies in the short-window inputs that have incomplete period observation, heterogeneous global spatial correlation, and cross-period superposition causality. To bridge this gap, we develop a novel Multi- Period Pattern Pre-training (MP3), a pl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.13119","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T09:48:18Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"d21ba903b1226833455c95752a012bfd6c6045b9578004419c135d64fbd31414","abstract_canon_sha256":"92581ef578a1638444ad956d624155d780525eea2369b1fac1016656bcfe9560"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:09:41.303943Z","signature_b64":"xTRX+PtKG6duo5BzG/Ji4RNMtiKF5nrFXGBIHd3CdIigRtqGia63t5QBE2UAnUTLu5HXZA22Hb1O6oOniom1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03b150d3df195aeebce293837caf0b6a3510690815bcd482ea14ed13295bf845","last_reissued_at":"2026-06-12T01:09:41.303300Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:09:41.303300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Chongshou Li, Lilan Peng, Qingren Yao, Tianrui Li, Yandi Liu","submitted_at":"2026-06-11T09:48:18Z","abstract_excerpt":"Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio-temporal data exhibits temporal mirage: similar short-window inputs have divergent future trends, and vice versa. Existing spatio-temporal graph neural networks (STGNNs) cannot effectively identify such mirages. We argue that the core reason lies in the short-window inputs that have incomplete period observation, heterogeneous global spatial correlation, and cross-period superposition causality. To bridge this gap, we develop a novel Multi- Period Pattern Pre-training (MP3), a pl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.13119","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/2606.13119/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.13119","created_at":"2026-06-12T01:09:41.303441+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.13119v1","created_at":"2026-06-12T01:09:41.303441+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.13119","created_at":"2026-06-12T01:09:41.303441+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOYVBU67DFNO","created_at":"2026-06-12T01:09:41.303441+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOYVBU67DFNO5PHC","created_at":"2026-06-12T01:09:41.303441+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOYVBU67","created_at":"2026-06-12T01:09:41.303441+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI","json":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI.json","graph_json":"https://pith.science/api/pith-number/AOYVBU67DFNO5PHCSOBXZLYLNI/graph.json","events_json":"https://pith.science/api/pith-number/AOYVBU67DFNO5PHCSOBXZLYLNI/events.json","paper":"https://pith.science/paper/AOYVBU67"},"agent_actions":{"view_html":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI","download_json":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI.json","view_paper":"https://pith.science/paper/AOYVBU67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.13119&json=true","fetch_graph":"https://pith.science/api/pith-number/AOYVBU67DFNO5PHCSOBXZLYLNI/graph.json","fetch_events":"https://pith.science/api/pith-number/AOYVBU67DFNO5PHCSOBXZLYLNI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI/action/storage_attestation","attest_author":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI/action/author_attestation","sign_citation":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI/action/citation_signature","submit_replication":"https://pith.science/pith/AOYVBU67DFNO5PHCSOBXZLYLNI/action/replication_record"}},"created_at":"2026-06-12T01:09:41.303441+00:00","updated_at":"2026-06-12T01:09:41.303441+00:00"}