{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:EF3QF4L47I46AOFCU7R7JHAATJ","short_pith_number":"pith:EF3QF4L4","canonical_record":{"source":{"id":"2104.04206","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-09T06:14:54Z","cross_cats_sorted":[],"title_canon_sha256":"ff50a22d5927972c9e75186e57315efe048e549b96c2fff5808e2767fe1ba92d","abstract_canon_sha256":"373339ae3ba873d323420e40740f845183177b21dc69c2562a7032e57224b188"},"schema_version":"1.0"},"canonical_sha256":"217702f17cfa39e038a2a7e3f49c009a704399c2c20fa2dd893fd8c142a7948f","source":{"kind":"arxiv","id":"2104.04206","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.04206","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"arxiv_version","alias_value":"2104.04206v1","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.04206","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_12","alias_value":"EF3QF4L47I46","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_16","alias_value":"EF3QF4L47I46AOFC","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_8","alias_value":"EF3QF4L4","created_at":"2026-07-05T02:41:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:EF3QF4L47I46AOFCU7R7JHAATJ","target":"record","payload":{"canonical_record":{"source":{"id":"2104.04206","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-09T06:14:54Z","cross_cats_sorted":[],"title_canon_sha256":"ff50a22d5927972c9e75186e57315efe048e549b96c2fff5808e2767fe1ba92d","abstract_canon_sha256":"373339ae3ba873d323420e40740f845183177b21dc69c2562a7032e57224b188"},"schema_version":"1.0"},"canonical_sha256":"217702f17cfa39e038a2a7e3f49c009a704399c2c20fa2dd893fd8c142a7948f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:41:32.351757Z","signature_b64":"wVnxhiIUnnbZ3h3OsNX29KL21vEswKSpKcKIqOD3Zz3USiJ3dhn7PdXH4u2Q3qidc0pGfRici6ZIZJXQCt8uBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"217702f17cfa39e038a2a7e3f49c009a704399c2c20fa2dd893fd8c142a7948f","last_reissued_at":"2026-07-05T02:41:32.351309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:41:32.351309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.04206","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":"2026-07-05T02:41:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QvF8EdnEpE5qiiR4ch1awc8HdodO23+BTYtqeiduQx+uLuVJiD5VgQoBDNmND4kUB2iIBnWSvv02xPEpxtGsAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:34:33.982128Z"},"content_sha256":"4480a2420fd2b59d918b1ead5f3112267a5ab044c169b5a71b2d9783a0c5c2f6","schema_version":"1.0","event_id":"sha256:4480a2420fd2b59d918b1ead5f3112267a5ab044c169b5a71b2d9783a0c5c2f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:EF3QF4L47I46AOFCU7R7JHAATJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Granger Causality Based Hierarchical Time Series Clustering for State Estimation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gregor P. Henze, Homagni Saha, Margarite Jacoby, Sin Yong Tan, Soumik Sarkar","submitted_at":"2021-04-09T06:14:54Z","abstract_excerpt":"Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often impractical due to large computational power requirements, and sufficiently robust algorithms to handle these cases are not common. We propose a hierarchical time series clustering technique based on symbolic dynamic filtering and Granger causality, which serves as a dimensionality red"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.04206","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/2104.04206/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":"2026-07-05T02:41:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X3VRaas0En1qttEWwh7aXVUBQup5gO4MkGkzH3ayizTA39Ub6PWZXI5QwlOc+jms3WS6o8eUGIEA5ErnRkyxDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:34:33.983198Z"},"content_sha256":"a4e7f71ada9186e4bbb769ecfec349b57e5d5b0ab19a5210cf4964000aedcb6c","schema_version":"1.0","event_id":"sha256:a4e7f71ada9186e4bbb769ecfec349b57e5d5b0ab19a5210cf4964000aedcb6c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EF3QF4L47I46AOFCU7R7JHAATJ/bundle.json","state_url":"https://pith.science/pith/EF3QF4L47I46AOFCU7R7JHAATJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EF3QF4L47I46AOFCU7R7JHAATJ/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":"2026-08-03T15:34:33Z","links":{"resolver":"https://pith.science/pith/EF3QF4L47I46AOFCU7R7JHAATJ","bundle":"https://pith.science/pith/EF3QF4L47I46AOFCU7R7JHAATJ/bundle.json","state":"https://pith.science/pith/EF3QF4L47I46AOFCU7R7JHAATJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EF3QF4L47I46AOFCU7R7JHAATJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:EF3QF4L47I46AOFCU7R7JHAATJ","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":"373339ae3ba873d323420e40740f845183177b21dc69c2562a7032e57224b188","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-09T06:14:54Z","title_canon_sha256":"ff50a22d5927972c9e75186e57315efe048e549b96c2fff5808e2767fe1ba92d"},"schema_version":"1.0","source":{"id":"2104.04206","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.04206","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"arxiv_version","alias_value":"2104.04206v1","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.04206","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_12","alias_value":"EF3QF4L47I46","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_16","alias_value":"EF3QF4L47I46AOFC","created_at":"2026-07-05T02:41:32Z"},{"alias_kind":"pith_short_8","alias_value":"EF3QF4L4","created_at":"2026-07-05T02:41:32Z"}],"graph_snapshots":[{"event_id":"sha256:a4e7f71ada9186e4bbb769ecfec349b57e5d5b0ab19a5210cf4964000aedcb6c","target":"graph","created_at":"2026-07-05T02:41:32Z","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/2104.04206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often impractical due to large computational power requirements, and sufficiently robust algorithms to handle these cases are not common. We propose a hierarchical time series clustering technique based on symbolic dynamic filtering and Granger causality, which serves as a dimensionality red","authors_text":"Gregor P. Henze, Homagni Saha, Margarite Jacoby, Sin Yong Tan, Soumik Sarkar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-09T06:14:54Z","title":"Granger Causality Based Hierarchical Time Series Clustering for State Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.04206","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":"sha256:4480a2420fd2b59d918b1ead5f3112267a5ab044c169b5a71b2d9783a0c5c2f6","target":"record","created_at":"2026-07-05T02:41:32Z","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":"373339ae3ba873d323420e40740f845183177b21dc69c2562a7032e57224b188","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-09T06:14:54Z","title_canon_sha256":"ff50a22d5927972c9e75186e57315efe048e549b96c2fff5808e2767fe1ba92d"},"schema_version":"1.0","source":{"id":"2104.04206","kind":"arxiv","version":1}},"canonical_sha256":"217702f17cfa39e038a2a7e3f49c009a704399c2c20fa2dd893fd8c142a7948f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"217702f17cfa39e038a2a7e3f49c009a704399c2c20fa2dd893fd8c142a7948f","first_computed_at":"2026-07-05T02:41:32.351309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:41:32.351309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wVnxhiIUnnbZ3h3OsNX29KL21vEswKSpKcKIqOD3Zz3USiJ3dhn7PdXH4u2Q3qidc0pGfRici6ZIZJXQCt8uBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:41:32.351757Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.04206","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4480a2420fd2b59d918b1ead5f3112267a5ab044c169b5a71b2d9783a0c5c2f6","sha256:a4e7f71ada9186e4bbb769ecfec349b57e5d5b0ab19a5210cf4964000aedcb6c"],"state_sha256":"6394ea50e827de56f35466f92f4e126a3a83bf38636ad215c2c83ac6283e4825"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZzGCuW3QcHVCMcrA6Dsf14FzuJDY33HkKsTKS0kM/Vt3kSXt16n1hRSm3PyMdnyVU2y8sK7JEqQQY8SPSs8XAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T15:34:33.990577Z","bundle_sha256":"000a2c7e48e5e9911f4e46260e072a57cd2126d058c441875e67d37262fb22c4"}}