{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7QBBOJJTAZXVPO4G6YA7R3HVHC","short_pith_number":"pith:7QBBOJJT","schema_version":"1.0","canonical_sha256":"fc02172533066f57bb86f601f8ecf5388a14960fb27927f14955d38d62748ac5","source":{"kind":"arxiv","id":"2406.03751","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dawei Cheng, Peiyuan Liu, Peng Zhu, Tao Dai, Yifan Hu","submitted_at":"2024-06-06T05:27:33Z","abstract_excerpt":"Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). While Transformer-based methods excel in capturing long-range dependencies, they suffer from high computational complexities and tend to overfit. Conversely, MLP-based methods offer computational efficiency and adeptness in modeling temporal dynamics, but they struggle with capturing complex temporal patterns effectively. To address these challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition (AMD) framework for TSF. Our framework decomposes time series into distinc"},"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":"2406.03751","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-06T05:27:33Z","cross_cats_sorted":[],"title_canon_sha256":"cfd8567f1fb4e3a8b9b34afb39927e39fb2a100b3b0fe09fd856013970850221","abstract_canon_sha256":"6ba503d17e5ae147096debf0f97eacf21d440eced468acf6ab29a21bc33b323c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:06.576949Z","signature_b64":"URcyyyqk+9pexmonttKXSSmOk2VsHT6PDW5I6C/bDeTjvBmD7Ho2FuE7rWTkqtVfQXT9ZkQKbGclevJI/Ab9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc02172533066f57bb86f601f8ecf5388a14960fb27927f14955d38d62748ac5","last_reissued_at":"2026-07-05T10:49:06.576477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:06.576477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dawei Cheng, Peiyuan Liu, Peng Zhu, Tao Dai, Yifan Hu","submitted_at":"2024-06-06T05:27:33Z","abstract_excerpt":"Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). While Transformer-based methods excel in capturing long-range dependencies, they suffer from high computational complexities and tend to overfit. Conversely, MLP-based methods offer computational efficiency and adeptness in modeling temporal dynamics, but they struggle with capturing complex temporal patterns effectively. To address these challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition (AMD) framework for TSF. Our framework decomposes time series into distinc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03751","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/2406.03751/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":"2406.03751","created_at":"2026-07-05T10:49:06.576532+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.03751v2","created_at":"2026-07-05T10:49:06.576532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03751","created_at":"2026-07-05T10:49:06.576532+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QBBOJJTAZXV","created_at":"2026-07-05T10:49:06.576532+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QBBOJJTAZXVPO4G","created_at":"2026-07-05T10:49:06.576532+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QBBOJJT","created_at":"2026-07-05T10:49:06.576532+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.18834","citing_title":"FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC","json":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC.json","graph_json":"https://pith.science/api/pith-number/7QBBOJJTAZXVPO4G6YA7R3HVHC/graph.json","events_json":"https://pith.science/api/pith-number/7QBBOJJTAZXVPO4G6YA7R3HVHC/events.json","paper":"https://pith.science/paper/7QBBOJJT"},"agent_actions":{"view_html":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC","download_json":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC.json","view_paper":"https://pith.science/paper/7QBBOJJT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.03751&json=true","fetch_graph":"https://pith.science/api/pith-number/7QBBOJJTAZXVPO4G6YA7R3HVHC/graph.json","fetch_events":"https://pith.science/api/pith-number/7QBBOJJTAZXVPO4G6YA7R3HVHC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC/action/storage_attestation","attest_author":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC/action/author_attestation","sign_citation":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC/action/citation_signature","submit_replication":"https://pith.science/pith/7QBBOJJTAZXVPO4G6YA7R3HVHC/action/replication_record"}},"created_at":"2026-07-05T10:49:06.576532+00:00","updated_at":"2026-07-05T10:49:06.576532+00:00"}