{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KW4XJ7ZZASHTN244FJGZUQY2GG","short_pith_number":"pith:KW4XJ7ZZ","schema_version":"1.0","canonical_sha256":"55b974ff39048f36eb9c2a4d9a431a31b0633ad0ac95610ab4eef100bd15b909","source":{"kind":"arxiv","id":"2402.17196","version":1},"attestation_state":"computed","paper":{"title":"Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Haimin Wang, Huseyin Cavus, Jason T. L. Wang, Ju Jing, Khalid A. Alobaid, Vania K. Jordanova, Vasyl Yurchyshyn, Yasser Abduallah","submitted_at":"2024-02-27T04:23:35Z","abstract_excerpt":"We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian "},"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":"2402.17196","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-02-27T04:23:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d3a40c3212affda19125154c4387482ed1eb657d49ac4f5cc555c978618abe3f","abstract_canon_sha256":"ec751245f3a099fcb05725fa38a9a979c784b6182967e29dce2c8e799f9d6031"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:43.960263Z","signature_b64":"i/JU8R5UAJnrzIS8ZBXJtQgsDFGgl6N867RcofW+zqsLZALr5ZSuNpAV7eT8futSDg+aF+Dh8heh3Bd9dH28DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55b974ff39048f36eb9c2a4d9a431a31b0633ad0ac95610ab4eef100bd15b909","last_reissued_at":"2026-07-05T07:49:43.959800Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:43.959800Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Haimin Wang, Huseyin Cavus, Jason T. L. Wang, Ju Jing, Khalid A. Alobaid, Vania K. Jordanova, Vasyl Yurchyshyn, Yasser Abduallah","submitted_at":"2024-02-27T04:23:35Z","abstract_excerpt":"We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.17196","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/2402.17196/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":"2402.17196","created_at":"2026-07-05T07:49:43.959855+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.17196v1","created_at":"2026-07-05T07:49:43.959855+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.17196","created_at":"2026-07-05T07:49:43.959855+00:00"},{"alias_kind":"pith_short_12","alias_value":"KW4XJ7ZZASHT","created_at":"2026-07-05T07:49:43.959855+00:00"},{"alias_kind":"pith_short_16","alias_value":"KW4XJ7ZZASHTN244","created_at":"2026-07-05T07:49:43.959855+00:00"},{"alias_kind":"pith_short_8","alias_value":"KW4XJ7ZZ","created_at":"2026-07-05T07:49:43.959855+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/KW4XJ7ZZASHTN244FJGZUQY2GG","json":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG.json","graph_json":"https://pith.science/api/pith-number/KW4XJ7ZZASHTN244FJGZUQY2GG/graph.json","events_json":"https://pith.science/api/pith-number/KW4XJ7ZZASHTN244FJGZUQY2GG/events.json","paper":"https://pith.science/paper/KW4XJ7ZZ"},"agent_actions":{"view_html":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG","download_json":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG.json","view_paper":"https://pith.science/paper/KW4XJ7ZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.17196&json=true","fetch_graph":"https://pith.science/api/pith-number/KW4XJ7ZZASHTN244FJGZUQY2GG/graph.json","fetch_events":"https://pith.science/api/pith-number/KW4XJ7ZZASHTN244FJGZUQY2GG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG/action/storage_attestation","attest_author":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG/action/author_attestation","sign_citation":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG/action/citation_signature","submit_replication":"https://pith.science/pith/KW4XJ7ZZASHTN244FJGZUQY2GG/action/replication_record"}},"created_at":"2026-07-05T07:49:43.959855+00:00","updated_at":"2026-07-05T07:49:43.959855+00:00"}