{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HR54QZG4BK7OB4LVPL3YMI3NKC","short_pith_number":"pith:HR54QZG4","schema_version":"1.0","canonical_sha256":"3c7bc864dc0abee0f1757af786236d5081e50efb2e5ca200b2c3758f6ecb2174","source":{"kind":"arxiv","id":"2607.04919","version":1},"attestation_state":"computed","paper":{"title":"When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Frank Simon, Nicholas Tan Jerome","submitted_at":"2026-07-06T10:48:11Z","abstract_excerpt":"Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic break-even analysis answering when this investment pays off. Across 30 benchmark datasets, we compare zero-shot and LoRA fine-tuned foundation models (Chronos, Moirai, Lag-Llama) against classical baselines (Naive, ETS, ARIMA, XGBoost) at six training set sizes from 2% to 100% of available data. Foundation models outperform classical methods at every evaluated training fraction on 15 of 30 datasets - GPU deployment is "},"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":"2607.04919","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T10:48:11Z","cross_cats_sorted":[],"title_canon_sha256":"f8b3a219911a2b48e14b26b6aab03d2bcbccbb426fef0d3742dd982f7872a3ff","abstract_canon_sha256":"02b4b76e87249bcecc420debf766a34d3942b66d88f351aed477902bec7c073c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:20:12.759145Z","signature_b64":"eMfS6NixyPLUdjucrFJQUSm8HkXSATeHz7EcuTvZI+bBlNbGsVObRfUfM1mYRIwdqdMBWilgFrhBCoJsSWN2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c7bc864dc0abee0f1757af786236d5081e50efb2e5ca200b2c3758f6ecb2174","last_reissued_at":"2026-07-07T02:20:12.758630Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:20:12.758630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Frank Simon, Nicholas Tan Jerome","submitted_at":"2026-07-06T10:48:11Z","abstract_excerpt":"Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic break-even analysis answering when this investment pays off. Across 30 benchmark datasets, we compare zero-shot and LoRA fine-tuned foundation models (Chronos, Moirai, Lag-Llama) against classical baselines (Naive, ETS, ARIMA, XGBoost) at six training set sizes from 2% to 100% of available data. Foundation models outperform classical methods at every evaluated training fraction on 15 of 30 datasets - GPU deployment is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04919","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/2607.04919/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":"2607.04919","created_at":"2026-07-07T02:20:12.758710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.04919v1","created_at":"2026-07-07T02:20:12.758710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04919","created_at":"2026-07-07T02:20:12.758710+00:00"},{"alias_kind":"pith_short_12","alias_value":"HR54QZG4BK7O","created_at":"2026-07-07T02:20:12.758710+00:00"},{"alias_kind":"pith_short_16","alias_value":"HR54QZG4BK7OB4LV","created_at":"2026-07-07T02:20:12.758710+00:00"},{"alias_kind":"pith_short_8","alias_value":"HR54QZG4","created_at":"2026-07-07T02:20:12.758710+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/HR54QZG4BK7OB4LVPL3YMI3NKC","json":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC.json","graph_json":"https://pith.science/api/pith-number/HR54QZG4BK7OB4LVPL3YMI3NKC/graph.json","events_json":"https://pith.science/api/pith-number/HR54QZG4BK7OB4LVPL3YMI3NKC/events.json","paper":"https://pith.science/paper/HR54QZG4"},"agent_actions":{"view_html":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC","download_json":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC.json","view_paper":"https://pith.science/paper/HR54QZG4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.04919&json=true","fetch_graph":"https://pith.science/api/pith-number/HR54QZG4BK7OB4LVPL3YMI3NKC/graph.json","fetch_events":"https://pith.science/api/pith-number/HR54QZG4BK7OB4LVPL3YMI3NKC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC/action/storage_attestation","attest_author":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC/action/author_attestation","sign_citation":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC/action/citation_signature","submit_replication":"https://pith.science/pith/HR54QZG4BK7OB4LVPL3YMI3NKC/action/replication_record"}},"created_at":"2026-07-07T02:20:12.758710+00:00","updated_at":"2026-07-07T02:20:12.758710+00:00"}