{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XZ3STUIF6HNSWX4VUVFRY63G7X","short_pith_number":"pith:XZ3STUIF","canonical_record":{"source":{"id":"1702.06712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-22T09:07:00Z","cross_cats_sorted":[],"title_canon_sha256":"a5cb7b2b44e1b7f6046e0233363e8e74d38dfdaefd87804ccb2b0aeca28b0e40","abstract_canon_sha256":"534a0e2c9202c401083455400fd20f648269497f4a95f7feb1da51d6493b9276"},"schema_version":"1.0"},"canonical_sha256":"be7729d105f1db2b5f95a54b1c7b66fde0f75a834f862a2c01f5d6974c729090","source":{"kind":"arxiv","id":"1702.06712","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.06712","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"arxiv_version","alias_value":"1702.06712v1","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.06712","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"pith_short_12","alias_value":"XZ3STUIF6HNS","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XZ3STUIF6HNSWX4V","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XZ3STUIF","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XZ3STUIF6HNSWX4VUVFRY63G7X","target":"record","payload":{"canonical_record":{"source":{"id":"1702.06712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-22T09:07:00Z","cross_cats_sorted":[],"title_canon_sha256":"a5cb7b2b44e1b7f6046e0233363e8e74d38dfdaefd87804ccb2b0aeca28b0e40","abstract_canon_sha256":"534a0e2c9202c401083455400fd20f648269497f4a95f7feb1da51d6493b9276"},"schema_version":"1.0"},"canonical_sha256":"be7729d105f1db2b5f95a54b1c7b66fde0f75a834f862a2c01f5d6974c729090","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:12.864600Z","signature_b64":"Zs+ydeDOhWXNEMVLDWYQ/1CkVR0To0AxFp6OXyqCRUd/80H89/wHGx+e5Hx0bsMs6AC344Chz7L+fIjiriUdCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be7729d105f1db2b5f95a54b1c7b66fde0f75a834f862a2c01f5d6974c729090","last_reissued_at":"2026-05-18T00:50:12.864169Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:12.864169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.06712","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":"2026-05-18T00:50:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t8koYijMneV9TBxjNWZhLcWhKiEClu6j3hd/Cn3CFzbzQHWs8sRHWIxxxipXvGy6BaE5i4M5JFAPdXN8ooGlDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T06:41:29.836449Z"},"content_sha256":"2c23c3bf78828bb1c446cb84f96c7bcbb34e358caf9288e5f804ddc13f89734d","schema_version":"1.0","event_id":"sha256:2c23c3bf78828bb1c446cb84f96c7bcbb34e358caf9288e5f804ddc13f89734d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XZ3STUIF6HNSWX4VUVFRY63G7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Atif Raza, Stefan Kramer","submitted_at":"2017-02-22T09:07:00Z","abstract_excerpt":"Shapelets are discriminative time series subsequences that allow generation of interpretable classification models, which provide faster and generally better classification than the nearest neighbor approach. However, the shapelet discovery process requires the evaluation of all possible subsequences of all time series in the training set, making it extremely computation intensive. Consequently, shapelet discovery for large time series datasets quickly becomes intractable. A number of improvements have been proposed to reduce the training time. These techniques use approximation or discretizat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.06712","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":""},"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":"2026-05-18T00:50:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o9SlI7tCi+uEzVoWogn7dF0zJ5/ZpRSZmAiP6GCq9BPSP88ESSItMTSMjG9Ihy3PBTw1uRsBQUUr0Q189lMbCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T06:41:29.836793Z"},"content_sha256":"1a13e790dfa73c3d647a06043cfe8763204edbea3f13c3ed9fc49e8ca86fed52","schema_version":"1.0","event_id":"sha256:1a13e790dfa73c3d647a06043cfe8763204edbea3f13c3ed9fc49e8ca86fed52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/bundle.json","state_url":"https://pith.science/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/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":"2026-06-24T06:41:29Z","links":{"resolver":"https://pith.science/pith/XZ3STUIF6HNSWX4VUVFRY63G7X","bundle":"https://pith.science/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/bundle.json","state":"https://pith.science/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XZ3STUIF6HNSWX4VUVFRY63G7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XZ3STUIF6HNSWX4VUVFRY63G7X","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":"534a0e2c9202c401083455400fd20f648269497f4a95f7feb1da51d6493b9276","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-22T09:07:00Z","title_canon_sha256":"a5cb7b2b44e1b7f6046e0233363e8e74d38dfdaefd87804ccb2b0aeca28b0e40"},"schema_version":"1.0","source":{"id":"1702.06712","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.06712","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"arxiv_version","alias_value":"1702.06712v1","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.06712","created_at":"2026-05-18T00:50:12Z"},{"alias_kind":"pith_short_12","alias_value":"XZ3STUIF6HNS","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XZ3STUIF6HNSWX4V","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XZ3STUIF","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:1a13e790dfa73c3d647a06043cfe8763204edbea3f13c3ed9fc49e8ca86fed52","target":"graph","created_at":"2026-05-18T00:50:12Z","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":"Shapelets are discriminative time series subsequences that allow generation of interpretable classification models, which provide faster and generally better classification than the nearest neighbor approach. However, the shapelet discovery process requires the evaluation of all possible subsequences of all time series in the training set, making it extremely computation intensive. Consequently, shapelet discovery for large time series datasets quickly becomes intractable. A number of improvements have been proposed to reduce the training time. These techniques use approximation or discretizat","authors_text":"Atif Raza, Stefan Kramer","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-22T09:07:00Z","title":"Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.06712","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":"sha256:2c23c3bf78828bb1c446cb84f96c7bcbb34e358caf9288e5f804ddc13f89734d","target":"record","created_at":"2026-05-18T00:50:12Z","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":"534a0e2c9202c401083455400fd20f648269497f4a95f7feb1da51d6493b9276","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-22T09:07:00Z","title_canon_sha256":"a5cb7b2b44e1b7f6046e0233363e8e74d38dfdaefd87804ccb2b0aeca28b0e40"},"schema_version":"1.0","source":{"id":"1702.06712","kind":"arxiv","version":1}},"canonical_sha256":"be7729d105f1db2b5f95a54b1c7b66fde0f75a834f862a2c01f5d6974c729090","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be7729d105f1db2b5f95a54b1c7b66fde0f75a834f862a2c01f5d6974c729090","first_computed_at":"2026-05-18T00:50:12.864169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:12.864169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zs+ydeDOhWXNEMVLDWYQ/1CkVR0To0AxFp6OXyqCRUd/80H89/wHGx+e5Hx0bsMs6AC344Chz7L+fIjiriUdCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:12.864600Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.06712","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c23c3bf78828bb1c446cb84f96c7bcbb34e358caf9288e5f804ddc13f89734d","sha256:1a13e790dfa73c3d647a06043cfe8763204edbea3f13c3ed9fc49e8ca86fed52"],"state_sha256":"acca471c9e919217ff469d3565574d0a8052abb9b090592dbd9d47837ede11bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+spyqlTHmjSp1XQA4U/MWYZUj2N2RLwk4r1cXXQdSiMSzezItocqqJKLhDdBukeXh9/OwvZfXFBKvuGEzCnDBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T06:41:29.838700Z","bundle_sha256":"8824727f7baa192fea59df7536fcf64adc8108962d35312dc3ec053e737cd39d"}}