{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GD5YRJITOSZEUZXWZFY5N765ZR","short_pith_number":"pith:GD5YRJIT","canonical_record":{"source":{"id":"2508.16641","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T04:06:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4b5f0340372a0af582770bf2fb1bc189c7f5fb164ef54af6c2036b63b5c04c19","abstract_canon_sha256":"d27b87998f5324e9694b3c6ba8c3b76f11519e433fe6f7b4a841e7b189ef498a"},"schema_version":"1.0"},"canonical_sha256":"30fb88a51374b24a66f6c971d6ffddcc7c1aaabb5ab48e6a44cacaba75d0bee7","source":{"kind":"arxiv","id":"2508.16641","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.16641","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"arxiv_version","alias_value":"2508.16641v1","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.16641","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_12","alias_value":"GD5YRJITOSZE","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_16","alias_value":"GD5YRJITOSZEUZXW","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_8","alias_value":"GD5YRJIT","created_at":"2026-07-05T11:58:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GD5YRJITOSZEUZXWZFY5N765ZR","target":"record","payload":{"canonical_record":{"source":{"id":"2508.16641","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T04:06:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4b5f0340372a0af582770bf2fb1bc189c7f5fb164ef54af6c2036b63b5c04c19","abstract_canon_sha256":"d27b87998f5324e9694b3c6ba8c3b76f11519e433fe6f7b4a841e7b189ef498a"},"schema_version":"1.0"},"canonical_sha256":"30fb88a51374b24a66f6c971d6ffddcc7c1aaabb5ab48e6a44cacaba75d0bee7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:00.617026Z","signature_b64":"nU+hT8dNgM8yVLVBeB1WzwzFWsArtpAOYTaBe3T5dU1xYsAvQkswdbqlQhMXioSQt+V5PqowXQmHDzlDLCCGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30fb88a51374b24a66f6c971d6ffddcc7c1aaabb5ab48e6a44cacaba75d0bee7","last_reissued_at":"2026-07-05T11:58:00.616522Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:00.616522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.16641","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:58:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FZxyLLLn1wAIMHtAH3lpr9CLY7Q3hVwG/C9Od16iUZMBfCqbrFVUJ84PZvptl3o7r0aTv5zECIBnpeowZKGvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T15:19:43.431707Z"},"content_sha256":"20d97abf6e0d118f79ed0593c387898f606f82e38d4d76891a03be6e50edcaeb","schema_version":"1.0","event_id":"sha256:20d97abf6e0d118f79ed0593c387898f606f82e38d4d76891a03be6e50edcaeb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GD5YRJITOSZEUZXWZFY5N765ZR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dhruv D. Modi, Rong Pan","submitted_at":"2025-08-18T04:06:26Z","abstract_excerpt":"Time series foundation models (TSFMs) such as Lag-Llama, TimeGPT, Chronos, MOMENT, UniTS, and TimesFM have shown strong generalization and zero-shot capabilities for time series forecasting, anomaly detection, classification, and imputation. Despite these advantages, their predictions still suffer from variance, domain-specific bias, and limited uncertainty quantification when deployed on real operational data. This paper investigates a suite of statistical and ensemble-based enhancement techniques, including bootstrap-based bagging, regression-based stacking, prediction interval construction,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.16641","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/2508.16641/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:58:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TfUCu0aQl8WXv41n8zpFC2v9FuyfJuPLB7oas9cNEY86YHN9N9CYwv2jZ7vHaGJtk8X4qZ/sMU85NInBQx1UAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T15:19:43.432480Z"},"content_sha256":"356d1e27687f921fedf7d0c073d5e0ebcd3b4335018f8ed3af6b2a024cfd1226","schema_version":"1.0","event_id":"sha256:356d1e27687f921fedf7d0c073d5e0ebcd3b4335018f8ed3af6b2a024cfd1226"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GD5YRJITOSZEUZXWZFY5N765ZR/bundle.json","state_url":"https://pith.science/pith/GD5YRJITOSZEUZXWZFY5N765ZR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GD5YRJITOSZEUZXWZFY5N765ZR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T15:19:43Z","links":{"resolver":"https://pith.science/pith/GD5YRJITOSZEUZXWZFY5N765ZR","bundle":"https://pith.science/pith/GD5YRJITOSZEUZXWZFY5N765ZR/bundle.json","state":"https://pith.science/pith/GD5YRJITOSZEUZXWZFY5N765ZR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GD5YRJITOSZEUZXWZFY5N765ZR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GD5YRJITOSZEUZXWZFY5N765ZR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d27b87998f5324e9694b3c6ba8c3b76f11519e433fe6f7b4a841e7b189ef498a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T04:06:26Z","title_canon_sha256":"4b5f0340372a0af582770bf2fb1bc189c7f5fb164ef54af6c2036b63b5c04c19"},"schema_version":"1.0","source":{"id":"2508.16641","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.16641","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"arxiv_version","alias_value":"2508.16641v1","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.16641","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_12","alias_value":"GD5YRJITOSZE","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_16","alias_value":"GD5YRJITOSZEUZXW","created_at":"2026-07-05T11:58:00Z"},{"alias_kind":"pith_short_8","alias_value":"GD5YRJIT","created_at":"2026-07-05T11:58:00Z"}],"graph_snapshots":[{"event_id":"sha256:356d1e27687f921fedf7d0c073d5e0ebcd3b4335018f8ed3af6b2a024cfd1226","target":"graph","created_at":"2026-07-05T11:58:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.16641/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series foundation models (TSFMs) such as Lag-Llama, TimeGPT, Chronos, MOMENT, UniTS, and TimesFM have shown strong generalization and zero-shot capabilities for time series forecasting, anomaly detection, classification, and imputation. Despite these advantages, their predictions still suffer from variance, domain-specific bias, and limited uncertainty quantification when deployed on real operational data. This paper investigates a suite of statistical and ensemble-based enhancement techniques, including bootstrap-based bagging, regression-based stacking, prediction interval construction,","authors_text":"Dhruv D. Modi, Rong Pan","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T04:06:26Z","title":"Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.16641","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:20d97abf6e0d118f79ed0593c387898f606f82e38d4d76891a03be6e50edcaeb","target":"record","created_at":"2026-07-05T11:58:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d27b87998f5324e9694b3c6ba8c3b76f11519e433fe6f7b4a841e7b189ef498a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T04:06:26Z","title_canon_sha256":"4b5f0340372a0af582770bf2fb1bc189c7f5fb164ef54af6c2036b63b5c04c19"},"schema_version":"1.0","source":{"id":"2508.16641","kind":"arxiv","version":1}},"canonical_sha256":"30fb88a51374b24a66f6c971d6ffddcc7c1aaabb5ab48e6a44cacaba75d0bee7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30fb88a51374b24a66f6c971d6ffddcc7c1aaabb5ab48e6a44cacaba75d0bee7","first_computed_at":"2026-07-05T11:58:00.616522Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:00.616522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nU+hT8dNgM8yVLVBeB1WzwzFWsArtpAOYTaBe3T5dU1xYsAvQkswdbqlQhMXioSQt+V5PqowXQmHDzlDLCCGDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:00.617026Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.16641","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20d97abf6e0d118f79ed0593c387898f606f82e38d4d76891a03be6e50edcaeb","sha256:356d1e27687f921fedf7d0c073d5e0ebcd3b4335018f8ed3af6b2a024cfd1226"],"state_sha256":"58b966d297d8ef2372399484d899e5994fbea7a991ca24564e2065ae1aba3d20"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OxAkEmaJk2uwMBE80IK1S9xQsmKSBGb0ci2psRoBaL/wLFqaet6EN2NPnNp17uvO+dHMu2qxSY9xn73mnu3KCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T15:19:43.438357Z","bundle_sha256":"652e936c9bfb7542d9671bf2d2c320a6d5eca9f84740737b50b96c80dd9cf1e2"}}