{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S2EJLIJAQ3SLA43VMFK7Q5RG25","short_pith_number":"pith:S2EJLIJA","schema_version":"1.0","canonical_sha256":"968895a12086e4b073756155f87626d766a6362eb409c29fbca759212b797cb3","source":{"kind":"arxiv","id":"2412.07997","version":1},"attestation_state":"computed","paper":{"title":"Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiajiang Shen, Qianyu Xu, Weiyan Wu","submitted_at":"2024-12-11T00:42:31Z","abstract_excerpt":"In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these challenges, we propose a weather prediction model based on a multi-scale convolutional CNN-LSTM-Attention architecture, specifically designed for time series forecasting of temperature data in China. The model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (L"},"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":"2412.07997","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-11T00:42:31Z","cross_cats_sorted":[],"title_canon_sha256":"af15966ab5f9ca8b35b51435c9f21df74daaea4fc550dbf604b36d4d54f5ab56","abstract_canon_sha256":"5a2fde5adffac0f55e9c66c9f73389a830e8d1e92c13fe7bbe10d9197f7b0ea7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:42.806656Z","signature_b64":"HHlEcqe1wKMosI1nw9RxFi2L3JYltj9+ykU04ZtCTJNLlnKJWNkgJgo2D57ietykfJiQOHbe/irsxzr/PpM9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"968895a12086e4b073756155f87626d766a6362eb409c29fbca759212b797cb3","last_reissued_at":"2026-07-05T09:47:42.806167Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:42.806167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiajiang Shen, Qianyu Xu, Weiyan Wu","submitted_at":"2024-12-11T00:42:31Z","abstract_excerpt":"In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these challenges, we propose a weather prediction model based on a multi-scale convolutional CNN-LSTM-Attention architecture, specifically designed for time series forecasting of temperature data in China. The model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07997","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/2412.07997/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":"2412.07997","created_at":"2026-07-05T09:47:42.806228+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.07997v1","created_at":"2026-07-05T09:47:42.806228+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07997","created_at":"2026-07-05T09:47:42.806228+00:00"},{"alias_kind":"pith_short_12","alias_value":"S2EJLIJAQ3SL","created_at":"2026-07-05T09:47:42.806228+00:00"},{"alias_kind":"pith_short_16","alias_value":"S2EJLIJAQ3SLA43V","created_at":"2026-07-05T09:47:42.806228+00:00"},{"alias_kind":"pith_short_8","alias_value":"S2EJLIJA","created_at":"2026-07-05T09:47:42.806228+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07437","citing_title":"ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier","ref_index":91,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25","json":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25.json","graph_json":"https://pith.science/api/pith-number/S2EJLIJAQ3SLA43VMFK7Q5RG25/graph.json","events_json":"https://pith.science/api/pith-number/S2EJLIJAQ3SLA43VMFK7Q5RG25/events.json","paper":"https://pith.science/paper/S2EJLIJA"},"agent_actions":{"view_html":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25","download_json":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25.json","view_paper":"https://pith.science/paper/S2EJLIJA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.07997&json=true","fetch_graph":"https://pith.science/api/pith-number/S2EJLIJAQ3SLA43VMFK7Q5RG25/graph.json","fetch_events":"https://pith.science/api/pith-number/S2EJLIJAQ3SLA43VMFK7Q5RG25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25/action/storage_attestation","attest_author":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25/action/author_attestation","sign_citation":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25/action/citation_signature","submit_replication":"https://pith.science/pith/S2EJLIJAQ3SLA43VMFK7Q5RG25/action/replication_record"}},"created_at":"2026-07-05T09:47:42.806228+00:00","updated_at":"2026-07-05T09:47:42.806228+00:00"}