{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HVSY46JBWPW32ZK7YFPMQZRKBE","short_pith_number":"pith:HVSY46JB","schema_version":"1.0","canonical_sha256":"3d658e7921b3edbd655fc15ec8662a093e480f5b8ca2aeb35cc3d229b9f69a6a","source":{"kind":"arxiv","id":"1803.10888","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alberto Lamadrid, Katya Scheinberg, Kostas Hatalis, Shalinee Kishore","submitted_at":"2018-03-29T01:05:54Z","abstract_excerpt":"Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing cons"},"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":"1803.10888","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-29T01:05:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9b35aaa07eb80b1538540b0c4b7223a62f57be2ae3d38c1aa13e537d4be74b71","abstract_canon_sha256":"e4af344587faa4c67d33fa54348048ee284828c88d1406e3796bfea81a033ebe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:48.750100Z","signature_b64":"UOFG5iiegd3Fgd9hmIaSRfnsZwZLjjAXJzp+LUB9D+pGqfV4JFBASsFo00WVUBbIZ5QrSaNxKh1V4TBa4JSwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d658e7921b3edbd655fc15ec8662a093e480f5b8ca2aeb35cc3d229b9f69a6a","last_reissued_at":"2026-05-18T00:19:48.749386Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:48.749386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alberto Lamadrid, Katya Scheinberg, Kostas Hatalis, Shalinee Kishore","submitted_at":"2018-03-29T01:05:54Z","abstract_excerpt":"Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing cons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10888","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":"1803.10888","created_at":"2026-05-18T00:19:48.749511+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.10888v1","created_at":"2026-05-18T00:19:48.749511+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10888","created_at":"2026-05-18T00:19:48.749511+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVSY46JBWPW3","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVSY46JBWPW32ZK7","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVSY46JB","created_at":"2026-05-18T12:32:28.185984+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/HVSY46JBWPW32ZK7YFPMQZRKBE","json":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE.json","graph_json":"https://pith.science/api/pith-number/HVSY46JBWPW32ZK7YFPMQZRKBE/graph.json","events_json":"https://pith.science/api/pith-number/HVSY46JBWPW32ZK7YFPMQZRKBE/events.json","paper":"https://pith.science/paper/HVSY46JB"},"agent_actions":{"view_html":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE","download_json":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE.json","view_paper":"https://pith.science/paper/HVSY46JB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.10888&json=true","fetch_graph":"https://pith.science/api/pith-number/HVSY46JBWPW32ZK7YFPMQZRKBE/graph.json","fetch_events":"https://pith.science/api/pith-number/HVSY46JBWPW32ZK7YFPMQZRKBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE/action/storage_attestation","attest_author":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE/action/author_attestation","sign_citation":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE/action/citation_signature","submit_replication":"https://pith.science/pith/HVSY46JBWPW32ZK7YFPMQZRKBE/action/replication_record"}},"created_at":"2026-05-18T00:19:48.749511+00:00","updated_at":"2026-05-18T00:19:48.749511+00:00"}