{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZY6VLBMGXD27FNUXWDQHQZLV3W","short_pith_number":"pith:ZY6VLBMG","schema_version":"1.0","canonical_sha256":"ce3d558586b8f5f2b697b0e0786575dd80f689e8558b3fd04901ec3aba3f34ff","source":{"kind":"arxiv","id":"2504.08940","version":1},"attestation_state":"computed","paper":{"title":"Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Grzegorz Dudek","submitted_at":"2025-04-11T19:43:11Z","abstract_excerpt":"In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting prob"},"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":"2504.08940","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-11T19:43:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"805f41917c03a04ac9c943cccebb56a0a2de386ab793fce9686916e95be9ab21","abstract_canon_sha256":"ab230499691d05aaea91627a97ef06f38d3be3415f9789beccdd4733f79869e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:01.873408Z","signature_b64":"JPtldumG1HSl5po7OqRqIDB8nqCn5wTXzH7jS53XA2y4ucnus9cr4n8uJIJrbJTXLKBQDB1kS325IvGvSw5tAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce3d558586b8f5f2b697b0e0786575dd80f689e8558b3fd04901ec3aba3f34ff","last_reissued_at":"2026-07-05T10:48:01.872951Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:01.872951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Grzegorz Dudek","submitted_at":"2025-04-11T19:43:11Z","abstract_excerpt":"In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08940","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/2504.08940/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":"2504.08940","created_at":"2026-07-05T10:48:01.873008+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.08940v1","created_at":"2026-07-05T10:48:01.873008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08940","created_at":"2026-07-05T10:48:01.873008+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZY6VLBMGXD27","created_at":"2026-07-05T10:48:01.873008+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZY6VLBMGXD27FNUX","created_at":"2026-07-05T10:48:01.873008+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZY6VLBMG","created_at":"2026-07-05T10:48:01.873008+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/ZY6VLBMGXD27FNUXWDQHQZLV3W","json":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W.json","graph_json":"https://pith.science/api/pith-number/ZY6VLBMGXD27FNUXWDQHQZLV3W/graph.json","events_json":"https://pith.science/api/pith-number/ZY6VLBMGXD27FNUXWDQHQZLV3W/events.json","paper":"https://pith.science/paper/ZY6VLBMG"},"agent_actions":{"view_html":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W","download_json":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W.json","view_paper":"https://pith.science/paper/ZY6VLBMG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.08940&json=true","fetch_graph":"https://pith.science/api/pith-number/ZY6VLBMGXD27FNUXWDQHQZLV3W/graph.json","fetch_events":"https://pith.science/api/pith-number/ZY6VLBMGXD27FNUXWDQHQZLV3W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W/action/storage_attestation","attest_author":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W/action/author_attestation","sign_citation":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W/action/citation_signature","submit_replication":"https://pith.science/pith/ZY6VLBMGXD27FNUXWDQHQZLV3W/action/replication_record"}},"created_at":"2026-07-05T10:48:01.873008+00:00","updated_at":"2026-07-05T10:48:01.873008+00:00"}