{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:K4AV3SJNP5PQU7S3F2BN4LUSTH","short_pith_number":"pith:K4AV3SJN","canonical_record":{"source":{"id":"2101.03268","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-09T01:23:18Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"dd07d0541da0c893a32564a2cf0207daa11130e616482652ac6d18f2a64ca0a6","abstract_canon_sha256":"e973805eacfd3695b635f38089be8089de3a312d7858ad9a8d376e5c3787ced8"},"schema_version":"1.0"},"canonical_sha256":"57015dc92d7f5f0a7e5b2e82de2e9299fc4d24c9e832ee775bf5668bd67d6d14","source":{"kind":"arxiv","id":"2101.03268","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.03268","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"arxiv_version","alias_value":"2101.03268v1","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.03268","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_12","alias_value":"K4AV3SJNP5PQ","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_16","alias_value":"K4AV3SJNP5PQU7S3","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_8","alias_value":"K4AV3SJN","created_at":"2026-07-05T08:13:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:K4AV3SJNP5PQU7S3F2BN4LUSTH","target":"record","payload":{"canonical_record":{"source":{"id":"2101.03268","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-09T01:23:18Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"dd07d0541da0c893a32564a2cf0207daa11130e616482652ac6d18f2a64ca0a6","abstract_canon_sha256":"e973805eacfd3695b635f38089be8089de3a312d7858ad9a8d376e5c3787ced8"},"schema_version":"1.0"},"canonical_sha256":"57015dc92d7f5f0a7e5b2e82de2e9299fc4d24c9e832ee775bf5668bd67d6d14","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:23.507723Z","signature_b64":"dwnVPAv67jgs+O9mhuPhN41mYd9Whp/S/PDnk5fnHgxXk8SLPaTiTspWXTHiANpOHWwGAZ8LoR1wKENSFlqeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57015dc92d7f5f0a7e5b2e82de2e9299fc4d24c9e832ee775bf5668bd67d6d14","last_reissued_at":"2026-07-05T08:13:23.507221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:23.507221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.03268","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":"2026-07-05T08:13:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXTkr1EDFOkXq5OYd1yaAQVO9VsR1rWSgX/WCGgf968trWBZtUp7LpkdAU1C/UFzJ2ZWPKUZJ2WMoIHn0YryAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:35:03.257374Z"},"content_sha256":"6e326fe9d4e21bd30949e6887c1617b6442ce5d98d624376a783025ef72a8dcb","schema_version":"1.0","event_id":"sha256:6e326fe9d4e21bd30949e6887c1617b6442ce5d98d624376a783025ef72a8dcb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:K4AV3SJNP5PQU7S3F2BN4LUSTH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modelling multi-scale state-switching functional data with hidden Markov models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Andrew W. Trites, Evan Sidrow, Ian Murphy, Marie Auger-M\\'eth\\'e, Nancy Heckman, Sarah M.E. Fortune","submitted_at":"2021-01-09T01:23:18Z","abstract_excerpt":"Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats the curves as observations from a hidden Markov model (HMM). The distribution of each curve is then defined by another fine-scale model which may involve auto-regression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.03268","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/2101.03268/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":"2026-07-05T08:13:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BAd4tv3DCHJ2cchv3KY/melJ80sYlns0tLCY1n6dhp3wrHx2ziA8IMeyAHv7RlcM3p/iHqUCT+6ug5upoDhACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:35:03.257780Z"},"content_sha256":"b0f7d98a61742ad4c9d3edc03c1af842a64347c354fda08ab8274fffe2165bdc","schema_version":"1.0","event_id":"sha256:b0f7d98a61742ad4c9d3edc03c1af842a64347c354fda08ab8274fffe2165bdc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/bundle.json","state_url":"https://pith.science/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/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":"2026-07-18T11:35:03Z","links":{"resolver":"https://pith.science/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH","bundle":"https://pith.science/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/bundle.json","state":"https://pith.science/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K4AV3SJNP5PQU7S3F2BN4LUSTH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:K4AV3SJNP5PQU7S3F2BN4LUSTH","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":"e973805eacfd3695b635f38089be8089de3a312d7858ad9a8d376e5c3787ced8","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-09T01:23:18Z","title_canon_sha256":"dd07d0541da0c893a32564a2cf0207daa11130e616482652ac6d18f2a64ca0a6"},"schema_version":"1.0","source":{"id":"2101.03268","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.03268","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"arxiv_version","alias_value":"2101.03268v1","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.03268","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_12","alias_value":"K4AV3SJNP5PQ","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_16","alias_value":"K4AV3SJNP5PQU7S3","created_at":"2026-07-05T08:13:23Z"},{"alias_kind":"pith_short_8","alias_value":"K4AV3SJN","created_at":"2026-07-05T08:13:23Z"}],"graph_snapshots":[{"event_id":"sha256:b0f7d98a61742ad4c9d3edc03c1af842a64347c354fda08ab8274fffe2165bdc","target":"graph","created_at":"2026-07-05T08:13:23Z","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/2101.03268/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats the curves as observations from a hidden Markov model (HMM). The distribution of each curve is then defined by another fine-scale model which may involve auto-regression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to ","authors_text":"Andrew W. Trites, Evan Sidrow, Ian Murphy, Marie Auger-M\\'eth\\'e, Nancy Heckman, Sarah M.E. Fortune","cross_cats":["stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-09T01:23:18Z","title":"Modelling multi-scale state-switching functional data with hidden Markov models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.03268","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":"sha256:6e326fe9d4e21bd30949e6887c1617b6442ce5d98d624376a783025ef72a8dcb","target":"record","created_at":"2026-07-05T08:13:23Z","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":"e973805eacfd3695b635f38089be8089de3a312d7858ad9a8d376e5c3787ced8","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-09T01:23:18Z","title_canon_sha256":"dd07d0541da0c893a32564a2cf0207daa11130e616482652ac6d18f2a64ca0a6"},"schema_version":"1.0","source":{"id":"2101.03268","kind":"arxiv","version":1}},"canonical_sha256":"57015dc92d7f5f0a7e5b2e82de2e9299fc4d24c9e832ee775bf5668bd67d6d14","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57015dc92d7f5f0a7e5b2e82de2e9299fc4d24c9e832ee775bf5668bd67d6d14","first_computed_at":"2026-07-05T08:13:23.507221Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:13:23.507221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dwnVPAv67jgs+O9mhuPhN41mYd9Whp/S/PDnk5fnHgxXk8SLPaTiTspWXTHiANpOHWwGAZ8LoR1wKENSFlqeDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:13:23.507723Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.03268","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6e326fe9d4e21bd30949e6887c1617b6442ce5d98d624376a783025ef72a8dcb","sha256:b0f7d98a61742ad4c9d3edc03c1af842a64347c354fda08ab8274fffe2165bdc"],"state_sha256":"99a440f5c230e16113fb4dc40bb4cf32fde3135739ff20e11b4a155104807951"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JiSM0zvd1JdMtmEzNaYdx9Gn7paYUH3uDtKdLPoZrxIDe6U6kxJIWLqic7Hi7uV2PyVBAElJssInq2lx5S0cCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T11:35:03.260447Z","bundle_sha256":"47928a2c11ba8f03863b26ed4ec44ab3e5e319cca163b63b484b1ab428de0a94"}}