{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:YLRGDLVUZBALPNBDU5MEEVVB3E","short_pith_number":"pith:YLRGDLVU","canonical_record":{"source":{"id":"1311.4500","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-18T19:26:01Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e53881db96c821af79a2472f9b907f276decbfbbf61a7254a97b5eae9e83f64a","abstract_canon_sha256":"d9e8a4916ca560fa6cbd3f62f891559d388fd741221d98b1becc0344a669fa8d"},"schema_version":"1.0"},"canonical_sha256":"c2e261aeb4c840b7b423a7584256a1d9059ad1905a6dcc3677bb486372c4c016","source":{"kind":"arxiv","id":"1311.4500","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.4500","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"arxiv_version","alias_value":"1311.4500v4","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.4500","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"pith_short_12","alias_value":"YLRGDLVUZBAL","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"YLRGDLVUZBALPNBD","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"YLRGDLVU","created_at":"2026-05-18T12:28:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:YLRGDLVUZBALPNBDU5MEEVVB3E","target":"record","payload":{"canonical_record":{"source":{"id":"1311.4500","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-18T19:26:01Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e53881db96c821af79a2472f9b907f276decbfbbf61a7254a97b5eae9e83f64a","abstract_canon_sha256":"d9e8a4916ca560fa6cbd3f62f891559d388fd741221d98b1becc0344a669fa8d"},"schema_version":"1.0"},"canonical_sha256":"c2e261aeb4c840b7b423a7584256a1d9059ad1905a6dcc3677bb486372c4c016","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:08.276561Z","signature_b64":"Uye1fNBLleeUyHvo4J01mLZhkPIoo5HQFHmRqx7hbyz5gEIEYlQmlavWG81kSxeQGcWIuvSflEktWgJat/U+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2e261aeb4c840b7b423a7584256a1d9059ad1905a6dcc3677bb486372c4c016","last_reissued_at":"2026-05-18T02:51:08.276177Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:08.276177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1311.4500","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:51:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7sxbcv0mPmY/N3opKE1KJa92ISP1GTKjz/YyrAP+fR5ZMcAWywRFwtgCXb/eQaxWoxkKyxUcJM3HfD7ZC13wCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:35:52.018829Z"},"content_sha256":"4d94666e1cc04d9f77e91d952a98ccc09fcffb40ebc9df8ac2d2a34bb9d0653f","schema_version":"1.0","event_id":"sha256:4d94666e1cc04d9f77e91d952a98ccc09fcffb40ebc9df8ac2d2a34bb9d0653f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:YLRGDLVUZBALPNBDU5MEEVVB3E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Time series prediction via aggregation : an oracle bound including numerical cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Andres Sanchez-Perez (LTCI)","submitted_at":"2013-11-18T19:26:01Z","abstract_excerpt":"We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.4500","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T02:51:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9a0NwWmV2RRx3kUOiG/hnlWqNQ90hMl9RcMUttFXgwLj6vk846OyqVFw4q1jguJxXdB3rXTOC/4YVRYupTvXBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:35:52.019226Z"},"content_sha256":"ff3c0378951fa2570eedfe26ac44f6fdfb9deaa203c60b649f3c357bff85de22","schema_version":"1.0","event_id":"sha256:ff3c0378951fa2570eedfe26ac44f6fdfb9deaa203c60b649f3c357bff85de22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/bundle.json","state_url":"https://pith.science/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/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":"2026-06-09T16:35:52Z","links":{"resolver":"https://pith.science/pith/YLRGDLVUZBALPNBDU5MEEVVB3E","bundle":"https://pith.science/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/bundle.json","state":"https://pith.science/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YLRGDLVUZBALPNBDU5MEEVVB3E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:YLRGDLVUZBALPNBDU5MEEVVB3E","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":"d9e8a4916ca560fa6cbd3f62f891559d388fd741221d98b1becc0344a669fa8d","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-18T19:26:01Z","title_canon_sha256":"e53881db96c821af79a2472f9b907f276decbfbbf61a7254a97b5eae9e83f64a"},"schema_version":"1.0","source":{"id":"1311.4500","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.4500","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"arxiv_version","alias_value":"1311.4500v4","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.4500","created_at":"2026-05-18T02:51:08Z"},{"alias_kind":"pith_short_12","alias_value":"YLRGDLVUZBAL","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"YLRGDLVUZBALPNBD","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"YLRGDLVU","created_at":"2026-05-18T12:28:06Z"}],"graph_snapshots":[{"event_id":"sha256:ff3c0378951fa2570eedfe26ac44f6fdfb9deaa203c60b649f3c357bff85de22","target":"graph","created_at":"2026-05-18T02:51:08Z","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"},"paper":{"abstract_excerpt":"We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In th","authors_text":"Andres Sanchez-Perez (LTCI)","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-18T19:26:01Z","title":"Time series prediction via aggregation : an oracle bound including numerical cost"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.4500","kind":"arxiv","version":4},"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":"sha256:4d94666e1cc04d9f77e91d952a98ccc09fcffb40ebc9df8ac2d2a34bb9d0653f","target":"record","created_at":"2026-05-18T02:51:08Z","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":"d9e8a4916ca560fa6cbd3f62f891559d388fd741221d98b1becc0344a669fa8d","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-18T19:26:01Z","title_canon_sha256":"e53881db96c821af79a2472f9b907f276decbfbbf61a7254a97b5eae9e83f64a"},"schema_version":"1.0","source":{"id":"1311.4500","kind":"arxiv","version":4}},"canonical_sha256":"c2e261aeb4c840b7b423a7584256a1d9059ad1905a6dcc3677bb486372c4c016","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2e261aeb4c840b7b423a7584256a1d9059ad1905a6dcc3677bb486372c4c016","first_computed_at":"2026-05-18T02:51:08.276177Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:51:08.276177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Uye1fNBLleeUyHvo4J01mLZhkPIoo5HQFHmRqx7hbyz5gEIEYlQmlavWG81kSxeQGcWIuvSflEktWgJat/U+Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:51:08.276561Z","signed_message":"canonical_sha256_bytes"},"source_id":"1311.4500","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d94666e1cc04d9f77e91d952a98ccc09fcffb40ebc9df8ac2d2a34bb9d0653f","sha256:ff3c0378951fa2570eedfe26ac44f6fdfb9deaa203c60b649f3c357bff85de22"],"state_sha256":"483a6715d189077224d6c164f8971209861fe432401107ef3d4da50de3b84413"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bLFvYLTqLW/10WninekckqscHpVhqFDb0V+XyGMCsKdp+o92dPXgyuhNxMzwCHmU2qhetJJboBCRbX2/WzF7Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T16:35:52.021188Z","bundle_sha256":"1d4c354ef2e7079eab4a0d7471416934fde6c6134c3285e9d85c0f2f48012538"}}