{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JGXF5BLC4LGO5VR6WVL6DSCGLG","short_pith_number":"pith:JGXF5BLC","schema_version":"1.0","canonical_sha256":"49ae5e8562e2cceed63eb557e1c84659b3576a22b4f2001fa9ba5975ec87c1fc","source":{"kind":"arxiv","id":"2503.12107","version":1},"attestation_state":"computed","paper":{"title":"ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdul Fatir Ansari, Caner Turkmen, Danielle C. Maddix, Hugo Senetaire, Huibin Shen, Lorenzo Stella, Michael Bohlke-Schneider, Oleksandr Shchur, Pedro Mercado, Sebastian Pineda Arango, Shubham Kapoor, Syama Sundar Rangapuram, Yuyang Wang","submitted_at":"2025-03-15T12:34:19Z","abstract_excerpt":"Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent advances in pretraining large language model architectures for time series forecasting have led to highly accurate forecasters. However, the majority of these models do not readily use covariates as they are often specific to a certain task or domain. This paper introduces a new method to incorporate"},"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":"2503.12107","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-15T12:34:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e7004f65b47f913c22eb580b1ab53670a9f25538df5ee8aae0981e23b1d864cd","abstract_canon_sha256":"df4ba3b1f92576bbd0a98c0081d89c013ead4203d076f8e191fcfc9e1c91ae02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:05.628396Z","signature_b64":"44qdatWriOoeLG8cTRPqMe3eWUivxRz9mboXqG31yEXPXVOv+RSpwUz9FNjELZ65G3BPMfVvaTgRyBUKFYCEAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49ae5e8562e2cceed63eb557e1c84659b3576a22b4f2001fa9ba5975ec87c1fc","last_reissued_at":"2026-07-05T10:32:05.627758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:05.627758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdul Fatir Ansari, Caner Turkmen, Danielle C. Maddix, Hugo Senetaire, Huibin Shen, Lorenzo Stella, Michael Bohlke-Schneider, Oleksandr Shchur, Pedro Mercado, Sebastian Pineda Arango, Shubham Kapoor, Syama Sundar Rangapuram, Yuyang Wang","submitted_at":"2025-03-15T12:34:19Z","abstract_excerpt":"Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent advances in pretraining large language model architectures for time series forecasting have led to highly accurate forecasters. However, the majority of these models do not readily use covariates as they are often specific to a certain task or domain. This paper introduces a new method to incorporate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.12107","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/2503.12107/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":"2503.12107","created_at":"2026-07-05T10:32:05.627834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.12107v1","created_at":"2026-07-05T10:32:05.627834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.12107","created_at":"2026-07-05T10:32:05.627834+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGXF5BLC4LGO","created_at":"2026-07-05T10:32:05.627834+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGXF5BLC4LGO5VR6","created_at":"2026-07-05T10:32:05.627834+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGXF5BLC","created_at":"2026-07-05T10:32:05.627834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01966","citing_title":"Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2603.04791","citing_title":"Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG","json":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG.json","graph_json":"https://pith.science/api/pith-number/JGXF5BLC4LGO5VR6WVL6DSCGLG/graph.json","events_json":"https://pith.science/api/pith-number/JGXF5BLC4LGO5VR6WVL6DSCGLG/events.json","paper":"https://pith.science/paper/JGXF5BLC"},"agent_actions":{"view_html":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG","download_json":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG.json","view_paper":"https://pith.science/paper/JGXF5BLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.12107&json=true","fetch_graph":"https://pith.science/api/pith-number/JGXF5BLC4LGO5VR6WVL6DSCGLG/graph.json","fetch_events":"https://pith.science/api/pith-number/JGXF5BLC4LGO5VR6WVL6DSCGLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG/action/storage_attestation","attest_author":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG/action/author_attestation","sign_citation":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG/action/citation_signature","submit_replication":"https://pith.science/pith/JGXF5BLC4LGO5VR6WVL6DSCGLG/action/replication_record"}},"created_at":"2026-07-05T10:32:05.627834+00:00","updated_at":"2026-07-05T10:32:05.627834+00:00"}