{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:DKW44NYHMGAKJHXS6BV3P7ZSSQ","short_pith_number":"pith:DKW44NYH","schema_version":"1.0","canonical_sha256":"1aadce37076180a49ef2f06bb7ff329403e5eee4f97754d6724d7a0e9fc9f40a","source":{"kind":"arxiv","id":"2003.09983","version":2},"attestation_state":"computed","paper":{"title":"A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Alexandre Street, Cristiano Fernandes, Marcelo Ruas","submitted_at":"2020-03-22T20:13:17Z","abstract_excerpt":"Modern decision-making processes require uncertainty-aware models, especially those relying on non-symmetric costs and risk-averse profiles. The objective of this work is to propose a dynamic model for the conditional non-parametric distribution function (CDF) to generate probabilistic forecasts for a renewable generation time series. To do that, we propose an adaptive non-parametric time-series model driven by a regularized multiple-quantile-regression (MQR) framework. In our approach, all regression models are jointly estimated through a single linear optimization problem that finds the glob"},"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":"2003.09983","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-03-22T20:13:17Z","cross_cats_sorted":[],"title_canon_sha256":"fe2c162d4eb366cfcf30c8cca98219cfb8128040ddbbdd36d61846a0fb85fca8","abstract_canon_sha256":"52b297d6914fc6d3b9c333f062ebb80141250347cb91d554114c179e82623618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:15:57.131607Z","signature_b64":"P4jY3Su/98Ah1q1uEmBBDVU43D1mmOyCFnYlrODnanaFhWxnlr3OJKI7iOfoGgJJnMVslHwAUTFMQXS2UrGtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aadce37076180a49ef2f06bb7ff329403e5eee4f97754d6724d7a0e9fc9f40a","last_reissued_at":"2026-07-05T02:15:57.131112Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:15:57.131112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Alexandre Street, Cristiano Fernandes, Marcelo Ruas","submitted_at":"2020-03-22T20:13:17Z","abstract_excerpt":"Modern decision-making processes require uncertainty-aware models, especially those relying on non-symmetric costs and risk-averse profiles. The objective of this work is to propose a dynamic model for the conditional non-parametric distribution function (CDF) to generate probabilistic forecasts for a renewable generation time series. To do that, we propose an adaptive non-parametric time-series model driven by a regularized multiple-quantile-regression (MQR) framework. In our approach, all regression models are jointly estimated through a single linear optimization problem that finds the glob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.09983","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/2003.09983/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":"2003.09983","created_at":"2026-07-05T02:15:57.131169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.09983v2","created_at":"2026-07-05T02:15:57.131169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.09983","created_at":"2026-07-05T02:15:57.131169+00:00"},{"alias_kind":"pith_short_12","alias_value":"DKW44NYHMGAK","created_at":"2026-07-05T02:15:57.131169+00:00"},{"alias_kind":"pith_short_16","alias_value":"DKW44NYHMGAKJHXS","created_at":"2026-07-05T02:15:57.131169+00:00"},{"alias_kind":"pith_short_8","alias_value":"DKW44NYH","created_at":"2026-07-05T02:15:57.131169+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/DKW44NYHMGAKJHXS6BV3P7ZSSQ","json":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ.json","graph_json":"https://pith.science/api/pith-number/DKW44NYHMGAKJHXS6BV3P7ZSSQ/graph.json","events_json":"https://pith.science/api/pith-number/DKW44NYHMGAKJHXS6BV3P7ZSSQ/events.json","paper":"https://pith.science/paper/DKW44NYH"},"agent_actions":{"view_html":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ","download_json":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ.json","view_paper":"https://pith.science/paper/DKW44NYH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.09983&json=true","fetch_graph":"https://pith.science/api/pith-number/DKW44NYHMGAKJHXS6BV3P7ZSSQ/graph.json","fetch_events":"https://pith.science/api/pith-number/DKW44NYHMGAKJHXS6BV3P7ZSSQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ/action/storage_attestation","attest_author":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ/action/author_attestation","sign_citation":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ/action/citation_signature","submit_replication":"https://pith.science/pith/DKW44NYHMGAKJHXS6BV3P7ZSSQ/action/replication_record"}},"created_at":"2026-07-05T02:15:57.131169+00:00","updated_at":"2026-07-05T02:15:57.131169+00:00"}