{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EIDRVWYFDH3UO6OOKTVBT6CZKI","short_pith_number":"pith:EIDRVWYF","schema_version":"1.0","canonical_sha256":"22071adb0519f74779ce54ea19f8595210977fe7af9120d8a63b46d34d24ef19","source":{"kind":"arxiv","id":"2211.02922","version":2},"attestation_state":"computed","paper":{"title":"Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Maarten de Hoop, Negar Erfanian, Santiago Segarra","submitted_at":"2022-11-05T14:55:36Z","abstract_excerpt":"Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to forecast one or multiple future events. In this work, we propose a new neural architecture for simultaneous multi-event forecasting of spatio-temporal point processes, utilizing transformers, augmented with normalizing flows and probabilis"},"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":"2211.02922","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-05T14:55:36Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"5c8c8681bf648c708241e4023e1e508e911cedb726f70613d69ff94539e532b5","abstract_canon_sha256":"a44d53c80a8db7ea6dc348cb42d6765338814cdcc90bf4c037a2f6b181312ffe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:36:32.006686Z","signature_b64":"Ig3ErQBFHGABzFTKQzPbq5UTZ22KfzlcDpYxpg9FleMD21MpN9TVTKrV97dMRlZu1mhi4EIvEEi/nPwNIJPrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22071adb0519f74779ce54ea19f8595210977fe7af9120d8a63b46d34d24ef19","last_reissued_at":"2026-07-05T05:36:32.006227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:36:32.006227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Maarten de Hoop, Negar Erfanian, Santiago Segarra","submitted_at":"2022-11-05T14:55:36Z","abstract_excerpt":"Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to forecast one or multiple future events. In this work, we propose a new neural architecture for simultaneous multi-event forecasting of spatio-temporal point processes, utilizing transformers, augmented with normalizing flows and probabilis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02922","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/2211.02922/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":"2211.02922","created_at":"2026-07-05T05:36:32.006282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.02922v2","created_at":"2026-07-05T05:36:32.006282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02922","created_at":"2026-07-05T05:36:32.006282+00:00"},{"alias_kind":"pith_short_12","alias_value":"EIDRVWYFDH3U","created_at":"2026-07-05T05:36:32.006282+00:00"},{"alias_kind":"pith_short_16","alias_value":"EIDRVWYFDH3UO6OO","created_at":"2026-07-05T05:36:32.006282+00:00"},{"alias_kind":"pith_short_8","alias_value":"EIDRVWYF","created_at":"2026-07-05T05:36:32.006282+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09341","citing_title":"Neural Spatiotemporal Point Processes: Trends and Challenges","ref_index":2016,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI","json":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI.json","graph_json":"https://pith.science/api/pith-number/EIDRVWYFDH3UO6OOKTVBT6CZKI/graph.json","events_json":"https://pith.science/api/pith-number/EIDRVWYFDH3UO6OOKTVBT6CZKI/events.json","paper":"https://pith.science/paper/EIDRVWYF"},"agent_actions":{"view_html":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI","download_json":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI.json","view_paper":"https://pith.science/paper/EIDRVWYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.02922&json=true","fetch_graph":"https://pith.science/api/pith-number/EIDRVWYFDH3UO6OOKTVBT6CZKI/graph.json","fetch_events":"https://pith.science/api/pith-number/EIDRVWYFDH3UO6OOKTVBT6CZKI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI/action/storage_attestation","attest_author":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI/action/author_attestation","sign_citation":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI/action/citation_signature","submit_replication":"https://pith.science/pith/EIDRVWYFDH3UO6OOKTVBT6CZKI/action/replication_record"}},"created_at":"2026-07-05T05:36:32.006282+00:00","updated_at":"2026-07-05T05:36:32.006282+00:00"}