{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EKBWTCDUBHEQI5YSXO7C4GJEEN","short_pith_number":"pith:EKBWTCDU","schema_version":"1.0","canonical_sha256":"228369887409c9047712bbbe2e1924234c0bbe4e8783a0c1610e4cb7eb5bdffd","source":{"kind":"arxiv","id":"2606.06010","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Oscillatory-State Alignment for Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LG","authors_text":"Chao Zha, Tao Guo, Xiangfei Qiu, Yinfei Xu, Zhangyao Song, Ziqiong Li","submitted_at":"2026-06-04T10:59:59Z","abstract_excerpt":"Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through predefined periods, global spectral components, or fixed learnable templates. However, real-world temporal dynamics are rarely rigidly periodic: oscillatory behavior often evolves through amplitude modulation, phase drift, and local frequency variation. Under these conditions, fixed-template periodic modeling can become fundamentally mismatched to the underlying temporal states. We propose AOSNET, a Hilbert-guided fo"},"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":"2606.06010","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-04T10:59:59Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"d52b3fa4e2d1ccac2473d213453e2696b9e30ab732efa461901b4ccf1b0cdf22","abstract_canon_sha256":"ed9a5e6aaba540f5fc341cceeee34da4f0338b5b6c46575cd54af8e678ffe103"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:30.280169Z","signature_b64":"+oD/f7D5/LFKvu6oB8I2Vclyx4eskiuWhW4XvgZZnQz2k3S7NBZRP4mKy5FObNzRP12ByE9CswJA0Ey3Az7MDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"228369887409c9047712bbbe2e1924234c0bbe4e8783a0c1610e4cb7eb5bdffd","last_reissued_at":"2026-06-05T01:15:30.279759Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:30.279759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Oscillatory-State Alignment for Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LG","authors_text":"Chao Zha, Tao Guo, Xiangfei Qiu, Yinfei Xu, Zhangyao Song, Ziqiong Li","submitted_at":"2026-06-04T10:59:59Z","abstract_excerpt":"Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through predefined periods, global spectral components, or fixed learnable templates. However, real-world temporal dynamics are rarely rigidly periodic: oscillatory behavior often evolves through amplitude modulation, phase drift, and local frequency variation. Under these conditions, fixed-template periodic modeling can become fundamentally mismatched to the underlying temporal states. We propose AOSNET, a Hilbert-guided fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06010","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/2606.06010/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":"2606.06010","created_at":"2026-06-05T01:15:30.279824+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06010v1","created_at":"2026-06-05T01:15:30.279824+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06010","created_at":"2026-06-05T01:15:30.279824+00:00"},{"alias_kind":"pith_short_12","alias_value":"EKBWTCDUBHEQ","created_at":"2026-06-05T01:15:30.279824+00:00"},{"alias_kind":"pith_short_16","alias_value":"EKBWTCDUBHEQI5YS","created_at":"2026-06-05T01:15:30.279824+00:00"},{"alias_kind":"pith_short_8","alias_value":"EKBWTCDU","created_at":"2026-06-05T01:15:30.279824+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/EKBWTCDUBHEQI5YSXO7C4GJEEN","json":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN.json","graph_json":"https://pith.science/api/pith-number/EKBWTCDUBHEQI5YSXO7C4GJEEN/graph.json","events_json":"https://pith.science/api/pith-number/EKBWTCDUBHEQI5YSXO7C4GJEEN/events.json","paper":"https://pith.science/paper/EKBWTCDU"},"agent_actions":{"view_html":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN","download_json":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN.json","view_paper":"https://pith.science/paper/EKBWTCDU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06010&json=true","fetch_graph":"https://pith.science/api/pith-number/EKBWTCDUBHEQI5YSXO7C4GJEEN/graph.json","fetch_events":"https://pith.science/api/pith-number/EKBWTCDUBHEQI5YSXO7C4GJEEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN/action/storage_attestation","attest_author":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN/action/author_attestation","sign_citation":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN/action/citation_signature","submit_replication":"https://pith.science/pith/EKBWTCDUBHEQI5YSXO7C4GJEEN/action/replication_record"}},"created_at":"2026-06-05T01:15:30.279824+00:00","updated_at":"2026-06-05T01:15:30.279824+00:00"}