{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:MB5AWDLALMFW2CDFNHKPMPJ7TX","short_pith_number":"pith:MB5AWDLA","schema_version":"1.0","canonical_sha256":"607a0b0d605b0b6d086569d4f63d3f9dde02489ac55a73dce706632e635b50ed","source":{"kind":"arxiv","id":"2204.11008","version":4},"attestation_state":"computed","paper":{"title":"Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Flora Salim, Hamid Menouar, Junshan Zhang, Shuo Wang, Wei Shao, Xiao Xiao, Yufan Kang, Zhaofeng Zhang, Zhiling Jin","submitted_at":"2022-04-23T06:51:37Z","abstract_excerpt":"Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, a"},"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":"2204.11008","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-23T06:51:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"099e2f6711d3fd5f5266bf9fe30323eae04c86cc887ec3b4b2c9c7d5cbd42258","abstract_canon_sha256":"93c1bfee7521f5cd8ca9f36c3227beb6e4776d8d500b8527fef4b4398a5985aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:41.720739Z","signature_b64":"YFA3rooESdigY14Ea7xcdiBf+bA8uKHJ+m4TuXozlZnRNc8kF5WTccltGK+S2tTRFXNED/PQ/HsxB3xbE79EAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"607a0b0d605b0b6d086569d4f63d3f9dde02489ac55a73dce706632e635b50ed","last_reissued_at":"2026-07-05T04:53:41.720274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:41.720274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Flora Salim, Hamid Menouar, Junshan Zhang, Shuo Wang, Wei Shao, Xiao Xiao, Yufan Kang, Zhaofeng Zhang, Zhiling Jin","submitted_at":"2022-04-23T06:51:37Z","abstract_excerpt":"Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11008","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/2204.11008/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":"2204.11008","created_at":"2026-07-05T04:53:41.720344+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.11008v4","created_at":"2026-07-05T04:53:41.720344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11008","created_at":"2026-07-05T04:53:41.720344+00:00"},{"alias_kind":"pith_short_12","alias_value":"MB5AWDLALMFW","created_at":"2026-07-05T04:53:41.720344+00:00"},{"alias_kind":"pith_short_16","alias_value":"MB5AWDLALMFW2CDF","created_at":"2026-07-05T04:53:41.720344+00:00"},{"alias_kind":"pith_short_8","alias_value":"MB5AWDLA","created_at":"2026-07-05T04:53:41.720344+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/MB5AWDLALMFW2CDFNHKPMPJ7TX","json":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX.json","graph_json":"https://pith.science/api/pith-number/MB5AWDLALMFW2CDFNHKPMPJ7TX/graph.json","events_json":"https://pith.science/api/pith-number/MB5AWDLALMFW2CDFNHKPMPJ7TX/events.json","paper":"https://pith.science/paper/MB5AWDLA"},"agent_actions":{"view_html":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX","download_json":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX.json","view_paper":"https://pith.science/paper/MB5AWDLA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.11008&json=true","fetch_graph":"https://pith.science/api/pith-number/MB5AWDLALMFW2CDFNHKPMPJ7TX/graph.json","fetch_events":"https://pith.science/api/pith-number/MB5AWDLALMFW2CDFNHKPMPJ7TX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX/action/storage_attestation","attest_author":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX/action/author_attestation","sign_citation":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX/action/citation_signature","submit_replication":"https://pith.science/pith/MB5AWDLALMFW2CDFNHKPMPJ7TX/action/replication_record"}},"created_at":"2026-07-05T04:53:41.720344+00:00","updated_at":"2026-07-05T04:53:41.720344+00:00"}