{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SCOSYKYPJCLAI324ZIPFM5ASCK","short_pith_number":"pith:SCOSYKYP","schema_version":"1.0","canonical_sha256":"909d2c2b0f4896046f5cca1e567412128211a37c29b2169b67815d1653440bc6","source":{"kind":"arxiv","id":"1908.08489","version":1},"attestation_state":"computed","paper":{"title":"Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mahdi Nasiri, Mehrdad Rostamzadeh, Sasan Barak","submitted_at":"2019-08-22T16:49:30Z","abstract_excerpt":"One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inpu"},"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":"1908.08489","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.ML","submitted_at":"2019-08-22T16:49:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a883c0c7e33ed0335198e925f81546214ce10fbfe13644c74b22b16081fd52b6","abstract_canon_sha256":"bc2763ce778976ae2a3d27ea1118f252888d5ba8dd9002ae5bfa03ca3be2e306"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:13.297022Z","signature_b64":"qM9X5tbicEL4PXLUh2/ufMZ3QrQJ6YektL+HThf+R6KTGJSVpWKanvvLZMdv9Kb2srvwZZkVpzZzK6IylHvdDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"909d2c2b0f4896046f5cca1e567412128211a37c29b2169b67815d1653440bc6","last_reissued_at":"2026-07-04T23:59:13.296655Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:13.296655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mahdi Nasiri, Mehrdad Rostamzadeh, Sasan Barak","submitted_at":"2019-08-22T16:49:30Z","abstract_excerpt":"One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inpu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08489","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.08489/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":"1908.08489","created_at":"2026-07-04T23:59:13.296720+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.08489v1","created_at":"2026-07-04T23:59:13.296720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08489","created_at":"2026-07-04T23:59:13.296720+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCOSYKYPJCLA","created_at":"2026-07-04T23:59:13.296720+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCOSYKYPJCLAI324","created_at":"2026-07-04T23:59:13.296720+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCOSYKYP","created_at":"2026-07-04T23:59:13.296720+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.13036","citing_title":"Forecast-Then-Optimize Deep Learning Methods","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK","json":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK.json","graph_json":"https://pith.science/api/pith-number/SCOSYKYPJCLAI324ZIPFM5ASCK/graph.json","events_json":"https://pith.science/api/pith-number/SCOSYKYPJCLAI324ZIPFM5ASCK/events.json","paper":"https://pith.science/paper/SCOSYKYP"},"agent_actions":{"view_html":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK","download_json":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK.json","view_paper":"https://pith.science/paper/SCOSYKYP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.08489&json=true","fetch_graph":"https://pith.science/api/pith-number/SCOSYKYPJCLAI324ZIPFM5ASCK/graph.json","fetch_events":"https://pith.science/api/pith-number/SCOSYKYPJCLAI324ZIPFM5ASCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK/action/storage_attestation","attest_author":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK/action/author_attestation","sign_citation":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK/action/citation_signature","submit_replication":"https://pith.science/pith/SCOSYKYPJCLAI324ZIPFM5ASCK/action/replication_record"}},"created_at":"2026-07-04T23:59:13.296720+00:00","updated_at":"2026-07-04T23:59:13.296720+00:00"}