{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:R2EZNZ2RAGTP2QJFSTNUYIBEEJ","short_pith_number":"pith:R2EZNZ2R","schema_version":"1.0","canonical_sha256":"8e8996e75101a6fd412594db4c2024227eae59b80410393f6ded44a6a4771d2a","source":{"kind":"arxiv","id":"2203.05757","version":2},"attestation_state":"computed","paper":{"title":"A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"astro-ph.SR","authors_text":"Hai Shu, Heng Li, Yuchen Dang, Ziqi Chen","submitted_at":"2022-03-11T05:11:31Z","abstract_excerpt":"Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-dee"},"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":"2203.05757","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2022-03-11T05:11:31Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3072a4ade47bc096a7a4b9a431aafd95da3db3277d5be1a77282a676788924ff","abstract_canon_sha256":"d3c1917b5f025e74d4d7d2b5d3fe69ac7447345da3b4673259ec756e7863c884"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:26:22.583890Z","signature_b64":"qbI5vuDlzYCSkgV+pnXpZTCFEAfB0yXfgKY2KP1uMmu5udjxS93I/B5OU2sak1Oq/TtMtxSY3XjYQEGHfTHjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e8996e75101a6fd412594db4c2024227eae59b80410393f6ded44a6a4771d2a","last_reissued_at":"2026-07-05T04:26:22.583402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:26:22.583402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"astro-ph.SR","authors_text":"Hai Shu, Heng Li, Yuchen Dang, Ziqi Chen","submitted_at":"2022-03-11T05:11:31Z","abstract_excerpt":"Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-dee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.05757","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/2203.05757/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":"2203.05757","created_at":"2026-07-05T04:26:22.583458+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.05757v2","created_at":"2026-07-05T04:26:22.583458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.05757","created_at":"2026-07-05T04:26:22.583458+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2EZNZ2RAGTP","created_at":"2026-07-05T04:26:22.583458+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2EZNZ2RAGTP2QJF","created_at":"2026-07-05T04:26:22.583458+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2EZNZ2R","created_at":"2026-07-05T04:26:22.583458+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/R2EZNZ2RAGTP2QJFSTNUYIBEEJ","json":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ.json","graph_json":"https://pith.science/api/pith-number/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/graph.json","events_json":"https://pith.science/api/pith-number/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/events.json","paper":"https://pith.science/paper/R2EZNZ2R"},"agent_actions":{"view_html":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ","download_json":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ.json","view_paper":"https://pith.science/paper/R2EZNZ2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.05757&json=true","fetch_graph":"https://pith.science/api/pith-number/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/graph.json","fetch_events":"https://pith.science/api/pith-number/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/action/storage_attestation","attest_author":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/action/author_attestation","sign_citation":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/action/citation_signature","submit_replication":"https://pith.science/pith/R2EZNZ2RAGTP2QJFSTNUYIBEEJ/action/replication_record"}},"created_at":"2026-07-05T04:26:22.583458+00:00","updated_at":"2026-07-05T04:26:22.583458+00:00"}