{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XRPFY6KDEBIXRZFVE2H37O3UT2","short_pith_number":"pith:XRPFY6KD","schema_version":"1.0","canonical_sha256":"bc5e5c7943205178e4b5268fbfbb749e83a2a5c378775c00fe8ac0f90313f567","source":{"kind":"arxiv","id":"1701.04485","version":1},"attestation_state":"computed","paper":{"title":"A Hierarchical Spatio-Temporal Analog Forecasting Model for Count Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Christopher K. Wikle, Joshua Millspaugh, Patrick L. McDermott","submitted_at":"2017-01-16T23:17:52Z","abstract_excerpt":"1. Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous work on analog forecasting has typically been presented in an empirical or heuristic context, as opposed to a formal statistical context. 2. The model presented here extends the model-based analog method of McDermott and Wikle (2016) by placing analog forecasting within a fully hierarchical"},"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":"1701.04485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-01-16T23:17:52Z","cross_cats_sorted":[],"title_canon_sha256":"3d8f0359ea27289b701edb8888a1bbae1728b564a277bfd16da03f786d3af21c","abstract_canon_sha256":"8ec66b6b92705a93714db184131957b3ea758f4fea995dbef09f68215aebbe0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:43.587827Z","signature_b64":"3v3eYarF7P74fnnLYmt1bJ984m0i+Wqm0dc9QVjso5WlfiMfBFKsVYkB/5+4Yl6X/nkKxEU6jM5m3GUFM3/yCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc5e5c7943205178e4b5268fbfbb749e83a2a5c378775c00fe8ac0f90313f567","last_reissued_at":"2026-05-18T00:52:43.587258Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:43.587258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hierarchical Spatio-Temporal Analog Forecasting Model for Count Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Christopher K. Wikle, Joshua Millspaugh, Patrick L. McDermott","submitted_at":"2017-01-16T23:17:52Z","abstract_excerpt":"1. Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous work on analog forecasting has typically been presented in an empirical or heuristic context, as opposed to a formal statistical context. 2. The model presented here extends the model-based analog method of McDermott and Wikle (2016) by placing analog forecasting within a fully hierarchical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04485","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":""},"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":"1701.04485","created_at":"2026-05-18T00:52:43.587350+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.04485v1","created_at":"2026-05-18T00:52:43.587350+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04485","created_at":"2026-05-18T00:52:43.587350+00:00"},{"alias_kind":"pith_short_12","alias_value":"XRPFY6KDEBIX","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XRPFY6KDEBIXRZFV","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XRPFY6KD","created_at":"2026-05-18T12:31:56.362134+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/XRPFY6KDEBIXRZFVE2H37O3UT2","json":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2.json","graph_json":"https://pith.science/api/pith-number/XRPFY6KDEBIXRZFVE2H37O3UT2/graph.json","events_json":"https://pith.science/api/pith-number/XRPFY6KDEBIXRZFVE2H37O3UT2/events.json","paper":"https://pith.science/paper/XRPFY6KD"},"agent_actions":{"view_html":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2","download_json":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2.json","view_paper":"https://pith.science/paper/XRPFY6KD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.04485&json=true","fetch_graph":"https://pith.science/api/pith-number/XRPFY6KDEBIXRZFVE2H37O3UT2/graph.json","fetch_events":"https://pith.science/api/pith-number/XRPFY6KDEBIXRZFVE2H37O3UT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2/action/storage_attestation","attest_author":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2/action/author_attestation","sign_citation":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2/action/citation_signature","submit_replication":"https://pith.science/pith/XRPFY6KDEBIXRZFVE2H37O3UT2/action/replication_record"}},"created_at":"2026-05-18T00:52:43.587350+00:00","updated_at":"2026-05-18T00:52:43.587350+00:00"}