{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LON5CCTVL5ZNKUFVKA7X5F7PU7","short_pith_number":"pith:LON5CCTV","schema_version":"1.0","canonical_sha256":"5b9bd10a755f72d550b5503f7e97efa7e3adab17b518dbb5c421952c78d38a18","source":{"kind":"arxiv","id":"1706.04394","version":1},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Forecasting by Coupled Stochastic Differential Equations: Applications to Solar Power","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Emil B. Iversen, Henrik Madsen, Jan Kleissl, Jan K. M{\\o}ller, Juan M. Morales, Rune Juhl","submitted_at":"2017-06-14T10:25:05Z","abstract_excerpt":"Spatio-temporal problems exist in many areas of knowledge and disciplines ranging from biology to engineering and physics. However, solution strategies based on classical statistical techniques often fall short due to the large number of parameters that are to be estimated and the huge amount of data that need to be handled. In this paper we apply known techniques in a novel way to provide a framework for spatio-temporal modeling which is both computationally efficient and has a low dimensional parameter space. We present a micro-to-macro approach whereby the local dynamics are first modeled 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":"1706.04394","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-06-14T10:25:05Z","cross_cats_sorted":[],"title_canon_sha256":"2f72bf5ada9295606314dd37068d86b159c84f1bdd8393e58059586efd8a1b00","abstract_canon_sha256":"c627e492a059e88df3bb0b8e5159a3ebfb8c90d22b499568f39d7fefac4c267b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:22.788104Z","signature_b64":"XJC75rVhXDkQTNlwHVNieFD/UN2d/lraVR3qPP3ZTo4ClFt10oQIIVASqUV4zV933+7M+O7Wf3kiaAkbjVaUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b9bd10a755f72d550b5503f7e97efa7e3adab17b518dbb5c421952c78d38a18","last_reissued_at":"2026-05-18T00:42:22.787579Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:22.787579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Forecasting by Coupled Stochastic Differential Equations: Applications to Solar Power","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Emil B. Iversen, Henrik Madsen, Jan Kleissl, Jan K. M{\\o}ller, Juan M. Morales, Rune Juhl","submitted_at":"2017-06-14T10:25:05Z","abstract_excerpt":"Spatio-temporal problems exist in many areas of knowledge and disciplines ranging from biology to engineering and physics. However, solution strategies based on classical statistical techniques often fall short due to the large number of parameters that are to be estimated and the huge amount of data that need to be handled. In this paper we apply known techniques in a novel way to provide a framework for spatio-temporal modeling which is both computationally efficient and has a low dimensional parameter space. We present a micro-to-macro approach whereby the local dynamics are first modeled a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04394","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":"1706.04394","created_at":"2026-05-18T00:42:22.787665+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.04394v1","created_at":"2026-05-18T00:42:22.787665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04394","created_at":"2026-05-18T00:42:22.787665+00:00"},{"alias_kind":"pith_short_12","alias_value":"LON5CCTVL5ZN","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LON5CCTVL5ZNKUFV","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LON5CCTV","created_at":"2026-05-18T12:31:28.150371+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/LON5CCTVL5ZNKUFVKA7X5F7PU7","json":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7.json","graph_json":"https://pith.science/api/pith-number/LON5CCTVL5ZNKUFVKA7X5F7PU7/graph.json","events_json":"https://pith.science/api/pith-number/LON5CCTVL5ZNKUFVKA7X5F7PU7/events.json","paper":"https://pith.science/paper/LON5CCTV"},"agent_actions":{"view_html":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7","download_json":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7.json","view_paper":"https://pith.science/paper/LON5CCTV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.04394&json=true","fetch_graph":"https://pith.science/api/pith-number/LON5CCTVL5ZNKUFVKA7X5F7PU7/graph.json","fetch_events":"https://pith.science/api/pith-number/LON5CCTVL5ZNKUFVKA7X5F7PU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7/action/storage_attestation","attest_author":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7/action/author_attestation","sign_citation":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7/action/citation_signature","submit_replication":"https://pith.science/pith/LON5CCTVL5ZNKUFVKA7X5F7PU7/action/replication_record"}},"created_at":"2026-05-18T00:42:22.787665+00:00","updated_at":"2026-05-18T00:42:22.787665+00:00"}