{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RVAT7KH4RIWB7I6M7FTHRSSPWR","short_pith_number":"pith:RVAT7KH4","schema_version":"1.0","canonical_sha256":"8d413fa8fc8a2c1fa3ccf96678ca4fb45c83d5f28b11933b883f63f34064f7bd","source":{"kind":"arxiv","id":"2110.10380","version":2},"attestation_state":"computed","paper":{"title":"Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Hongkyu Lim, Hyeshin Chu, Hyunwook Lee, Seungmin Jin, Sungahn Ko","submitted_at":"2021-10-20T05:24:21Z","abstract_excerpt":"Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve this challenging problem with a focus on learning spatio-temporal dependencies of roads. In this work, we propose a new perspective of converting the forecasting problem into a pattern matching task, assuming that large data can be represented by a set of patterns. To evaluate the validness of the new perspective, we design a novel traffic forecasting model, called Pattern-Matching Memory Networks (PM-MemNet), which"},"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":"2110.10380","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-20T05:24:21Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"f62370616a04dba93f16cdae58575c6748a32e880629c7af643e2a719f34464e","abstract_canon_sha256":"730735ec044abeb6166e6cc7039f82954699cdfd275e9b5d5c83b30c5d4cdea4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:02:40.054991Z","signature_b64":"djy4WGK8Sy9HcffLwUjoCqv8n/wdWNko56+kKrEvS18n3Q/EQyrZDTuDQp3H4pKhnGEQXczWDAXFadpznph7BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d413fa8fc8a2c1fa3ccf96678ca4fb45c83d5f28b11933b883f63f34064f7bd","last_reissued_at":"2026-07-05T04:02:40.054380Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:02:40.054380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Hongkyu Lim, Hyeshin Chu, Hyunwook Lee, Seungmin Jin, Sungahn Ko","submitted_at":"2021-10-20T05:24:21Z","abstract_excerpt":"Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve this challenging problem with a focus on learning spatio-temporal dependencies of roads. In this work, we propose a new perspective of converting the forecasting problem into a pattern matching task, assuming that large data can be represented by a set of patterns. To evaluate the validness of the new perspective, we design a novel traffic forecasting model, called Pattern-Matching Memory Networks (PM-MemNet), which"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.10380","kind":"arxiv","version":2},"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/2110.10380/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":"2110.10380","created_at":"2026-07-05T04:02:40.054439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.10380v2","created_at":"2026-07-05T04:02:40.054439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.10380","created_at":"2026-07-05T04:02:40.054439+00:00"},{"alias_kind":"pith_short_12","alias_value":"RVAT7KH4RIWB","created_at":"2026-07-05T04:02:40.054439+00:00"},{"alias_kind":"pith_short_16","alias_value":"RVAT7KH4RIWB7I6M","created_at":"2026-07-05T04:02:40.054439+00:00"},{"alias_kind":"pith_short_8","alias_value":"RVAT7KH4","created_at":"2026-07-05T04:02:40.054439+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/RVAT7KH4RIWB7I6M7FTHRSSPWR","json":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR.json","graph_json":"https://pith.science/api/pith-number/RVAT7KH4RIWB7I6M7FTHRSSPWR/graph.json","events_json":"https://pith.science/api/pith-number/RVAT7KH4RIWB7I6M7FTHRSSPWR/events.json","paper":"https://pith.science/paper/RVAT7KH4"},"agent_actions":{"view_html":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR","download_json":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR.json","view_paper":"https://pith.science/paper/RVAT7KH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.10380&json=true","fetch_graph":"https://pith.science/api/pith-number/RVAT7KH4RIWB7I6M7FTHRSSPWR/graph.json","fetch_events":"https://pith.science/api/pith-number/RVAT7KH4RIWB7I6M7FTHRSSPWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR/action/storage_attestation","attest_author":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR/action/author_attestation","sign_citation":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR/action/citation_signature","submit_replication":"https://pith.science/pith/RVAT7KH4RIWB7I6M7FTHRSSPWR/action/replication_record"}},"created_at":"2026-07-05T04:02:40.054439+00:00","updated_at":"2026-07-05T04:02:40.054439+00:00"}