{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D232QB26ZBXCFXXVDKPKVCKHEE","short_pith_number":"pith:D232QB26","schema_version":"1.0","canonical_sha256":"1eb7a8075ec86e22def51a9eaa8947212e732f525a22c36e1117d7a659eeb190","source":{"kind":"arxiv","id":"2509.06060","version":1},"attestation_state":"computed","paper":{"title":"ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chengqing Yu, Fei Wang, Xueqi Cheng, Yisong Fu, Yongjun Xu, Yujie Li, Zezhi Shao, Zhulin An","submitted_at":"2025-09-07T13:57:14Z","abstract_excerpt":"Recent advancements in deep learning models for time series forecasting have been significant. These models often leverage fundamental time series properties such as seasonality and non-stationarity, which may suggest an intrinsic link between model performance and data properties. However, existing benchmark datasets fail to offer diverse and well-defined temporal patterns, restricting the systematic evaluation of such connections. Additionally, there is no effective model recommendation approach, leading to high time and cost expenditures when testing different architectures across different"},"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":"2509.06060","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-07T13:57:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9d5ca6758d3d0aa278d7ffed7e737db0cea4da135646d93e24c5577431e5d56c","abstract_canon_sha256":"6a12e395bf8e0b07d046eb5933dcff550e5996d04165e1b09cb0b62b5eb3fe9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:29.250226Z","signature_b64":"4350sEejrFzndIXmmXmQHQTN39X1VmSTNiLVDr1TsdlpC26a2ISSZvj+1+mwlVpxE+Iyn9g16URKwvz+2VGSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1eb7a8075ec86e22def51a9eaa8947212e732f525a22c36e1117d7a659eeb190","last_reissued_at":"2026-07-05T12:06:29.249749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:29.249749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chengqing Yu, Fei Wang, Xueqi Cheng, Yisong Fu, Yongjun Xu, Yujie Li, Zezhi Shao, Zhulin An","submitted_at":"2025-09-07T13:57:14Z","abstract_excerpt":"Recent advancements in deep learning models for time series forecasting have been significant. These models often leverage fundamental time series properties such as seasonality and non-stationarity, which may suggest an intrinsic link between model performance and data properties. However, existing benchmark datasets fail to offer diverse and well-defined temporal patterns, restricting the systematic evaluation of such connections. Additionally, there is no effective model recommendation approach, leading to high time and cost expenditures when testing different architectures across different"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06060","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/2509.06060/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":"2509.06060","created_at":"2026-07-05T12:06:29.249804+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06060v1","created_at":"2026-07-05T12:06:29.249804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06060","created_at":"2026-07-05T12:06:29.249804+00:00"},{"alias_kind":"pith_short_12","alias_value":"D232QB26ZBXC","created_at":"2026-07-05T12:06:29.249804+00:00"},{"alias_kind":"pith_short_16","alias_value":"D232QB26ZBXCFXXV","created_at":"2026-07-05T12:06:29.249804+00:00"},{"alias_kind":"pith_short_8","alias_value":"D232QB26","created_at":"2026-07-05T12:06:29.249804+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01918","citing_title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","ref_index":209,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE","json":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE.json","graph_json":"https://pith.science/api/pith-number/D232QB26ZBXCFXXVDKPKVCKHEE/graph.json","events_json":"https://pith.science/api/pith-number/D232QB26ZBXCFXXVDKPKVCKHEE/events.json","paper":"https://pith.science/paper/D232QB26"},"agent_actions":{"view_html":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE","download_json":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE.json","view_paper":"https://pith.science/paper/D232QB26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06060&json=true","fetch_graph":"https://pith.science/api/pith-number/D232QB26ZBXCFXXVDKPKVCKHEE/graph.json","fetch_events":"https://pith.science/api/pith-number/D232QB26ZBXCFXXVDKPKVCKHEE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE/action/storage_attestation","attest_author":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE/action/author_attestation","sign_citation":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE/action/citation_signature","submit_replication":"https://pith.science/pith/D232QB26ZBXCFXXVDKPKVCKHEE/action/replication_record"}},"created_at":"2026-07-05T12:06:29.249804+00:00","updated_at":"2026-07-05T12:06:29.249804+00:00"}