{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:76XAFQ3LXSVDBOCDTSPJCD4RCX","short_pith_number":"pith:76XAFQ3L","schema_version":"1.0","canonical_sha256":"ffae02c36bbcaa30b8439c9e910f9115d4a56fe8ef4f0e19e511bff4830cc787","source":{"kind":"arxiv","id":"2505.19090","version":1},"attestation_state":"computed","paper":{"title":"CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Changhua Pei, Dan Pei, Gaogang Xie, Haotian Si, Jianhui Li","submitted_at":"2025-05-25T11:01:53Z","abstract_excerpt":"Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight time series forecasting model. Instead of learning the embedding of the shapes, CMoS directly models the spatial correlations between different time series chunks. Additionally, we introduce a Correlation Mixing technique that enables the model to capture diverse spatial correlations with minimal parameters, and an optional Periodicity Injection technique to ensure faster convergence. Despite utilizing as low as 1% o"},"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":"2505.19090","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T11:01:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d21d652d35543445fc6065ed2be69958cb3f1f52a16419f103e6ee8c76119fc9","abstract_canon_sha256":"58ed84514b2b99178f369d8b21f799bf0bf118808177fca3d83c6206850b54e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:24.714204Z","signature_b64":"nRpB+5OqUauiPsbcidORnCqfGZpxSa+AMCcockwtNUwg+PdOmIL4JuV4i49p/KmZkuoVVPD+A/xM8Uy0PkNSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffae02c36bbcaa30b8439c9e910f9115d4a56fe8ef4f0e19e511bff4830cc787","last_reissued_at":"2026-07-05T11:09:24.713699Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:24.713699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Changhua Pei, Dan Pei, Gaogang Xie, Haotian Si, Jianhui Li","submitted_at":"2025-05-25T11:01:53Z","abstract_excerpt":"Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight time series forecasting model. Instead of learning the embedding of the shapes, CMoS directly models the spatial correlations between different time series chunks. Additionally, we introduce a Correlation Mixing technique that enables the model to capture diverse spatial correlations with minimal parameters, and an optional Periodicity Injection technique to ensure faster convergence. Despite utilizing as low as 1% o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19090","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/2505.19090/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":"2505.19090","created_at":"2026-07-05T11:09:24.713761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.19090v1","created_at":"2026-07-05T11:09:24.713761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19090","created_at":"2026-07-05T11:09:24.713761+00:00"},{"alias_kind":"pith_short_12","alias_value":"76XAFQ3LXSVD","created_at":"2026-07-05T11:09:24.713761+00:00"},{"alias_kind":"pith_short_16","alias_value":"76XAFQ3LXSVDBOCD","created_at":"2026-07-05T11:09:24.713761+00:00"},{"alias_kind":"pith_short_8","alias_value":"76XAFQ3L","created_at":"2026-07-05T11:09:24.713761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.00297","citing_title":"From Observations to States: Latent Time Series Forecasting","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08289","citing_title":"What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies","ref_index":109,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX","json":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX.json","graph_json":"https://pith.science/api/pith-number/76XAFQ3LXSVDBOCDTSPJCD4RCX/graph.json","events_json":"https://pith.science/api/pith-number/76XAFQ3LXSVDBOCDTSPJCD4RCX/events.json","paper":"https://pith.science/paper/76XAFQ3L"},"agent_actions":{"view_html":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX","download_json":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX.json","view_paper":"https://pith.science/paper/76XAFQ3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.19090&json=true","fetch_graph":"https://pith.science/api/pith-number/76XAFQ3LXSVDBOCDTSPJCD4RCX/graph.json","fetch_events":"https://pith.science/api/pith-number/76XAFQ3LXSVDBOCDTSPJCD4RCX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX/action/storage_attestation","attest_author":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX/action/author_attestation","sign_citation":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX/action/citation_signature","submit_replication":"https://pith.science/pith/76XAFQ3LXSVDBOCDTSPJCD4RCX/action/replication_record"}},"created_at":"2026-07-05T11:09:24.713761+00:00","updated_at":"2026-07-05T11:09:24.713761+00:00"}