{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BXF6GOJXKL7LQCCZD4TFV72LLT","short_pith_number":"pith:BXF6GOJX","schema_version":"1.0","canonical_sha256":"0dcbe3393752feb808591f265aff4b5ccac84d662988eecd66bd77e608d754fd","source":{"kind":"arxiv","id":"2410.14963","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Chi-Han Lee, Fei Wang, Yuchen Zhang, Yuhao Gong","submitted_at":"2024-10-19T03:38:53Z","abstract_excerpt":"As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates exce"},"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":"2410.14963","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-19T03:38:53Z","cross_cats_sorted":["cs.DC","physics.ao-ph"],"title_canon_sha256":"2964c3ea9b05e21cc89c67a87c74ba6f6ea0f8029bdbca11d7635162f889e74d","abstract_canon_sha256":"83fb9074729794dc96c129df77115b50dac559935594ab09a3866ef7b78e2917"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:47.726183Z","signature_b64":"HmqdOX81+wsororOizhyPE/9APT4OPhGMvDKxCTUGkQ+1YU+Cfm7xTlnKpPKJzhAEI0FEVqUFWmc1b7dlQbaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0dcbe3393752feb808591f265aff4b5ccac84d662988eecd66bd77e608d754fd","last_reissued_at":"2026-07-05T09:22:47.725641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:47.725641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Chi-Han Lee, Fei Wang, Yuchen Zhang, Yuhao Gong","submitted_at":"2024-10-19T03:38:53Z","abstract_excerpt":"As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates exce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14963","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/2410.14963/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":"2410.14963","created_at":"2026-07-05T09:22:47.725700+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14963v1","created_at":"2026-07-05T09:22:47.725700+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14963","created_at":"2026-07-05T09:22:47.725700+00:00"},{"alias_kind":"pith_short_12","alias_value":"BXF6GOJXKL7L","created_at":"2026-07-05T09:22:47.725700+00:00"},{"alias_kind":"pith_short_16","alias_value":"BXF6GOJXKL7LQCCZ","created_at":"2026-07-05T09:22:47.725700+00:00"},{"alias_kind":"pith_short_8","alias_value":"BXF6GOJX","created_at":"2026-07-05T09:22:47.725700+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06336","citing_title":"Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT","json":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT.json","graph_json":"https://pith.science/api/pith-number/BXF6GOJXKL7LQCCZD4TFV72LLT/graph.json","events_json":"https://pith.science/api/pith-number/BXF6GOJXKL7LQCCZD4TFV72LLT/events.json","paper":"https://pith.science/paper/BXF6GOJX"},"agent_actions":{"view_html":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT","download_json":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT.json","view_paper":"https://pith.science/paper/BXF6GOJX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14963&json=true","fetch_graph":"https://pith.science/api/pith-number/BXF6GOJXKL7LQCCZD4TFV72LLT/graph.json","fetch_events":"https://pith.science/api/pith-number/BXF6GOJXKL7LQCCZD4TFV72LLT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT/action/storage_attestation","attest_author":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT/action/author_attestation","sign_citation":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT/action/citation_signature","submit_replication":"https://pith.science/pith/BXF6GOJXKL7LQCCZD4TFV72LLT/action/replication_record"}},"created_at":"2026-07-05T09:22:47.725700+00:00","updated_at":"2026-07-05T09:22:47.725700+00:00"}