{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7LY24TNY4MHPRLCZLESKWOZCSP","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":"608fd59091da9dd0e4dae147546a93f039ac4b5198e06617ea8048263f41f32d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T23:13:46Z","title_canon_sha256":"f5b75b37a212ab156ee8afc71b0b5567ff5f24c50f9716c1afd45c05bcb63c4d"},"schema_version":"1.0","source":{"id":"1907.11815","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11815","created_at":"2026-07-05T02:38:27Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11815v1","created_at":"2026-07-05T02:38:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11815","created_at":"2026-07-05T02:38:27Z"},{"alias_kind":"pith_short_12","alias_value":"7LY24TNY4MHP","created_at":"2026-07-05T02:38:27Z"},{"alias_kind":"pith_short_16","alias_value":"7LY24TNY4MHPRLCZ","created_at":"2026-07-05T02:38:27Z"},{"alias_kind":"pith_short_8","alias_value":"7LY24TNY","created_at":"2026-07-05T02:38:27Z"}],"graph_snapshots":[{"event_id":"sha256:7ce9ee653fd0484e6a6c3544acf3c32d88172c8a2c028344ad84904270584db4","target":"graph","created_at":"2026-07-05T02:38:27Z","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/1907.11815/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as well as the best performing dictionary based classifier. A recent addition to the category, the Word Extraction for Time Series Classification (WEASEL), claims an improvement on this performance. Both of these algorithms however have non-trivial scalability issues, taking a consid","authors_text":"Anthony Bagnall, Matthew Middlehurst, William Vickers","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T23:13:46Z","title":"Scalable Dictionary Classifiers for Time Series Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11815","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:eb76b9b8e0851262dcbdc002a24bf0e5814de14b1d68a51e77dac22ea91130fa","target":"record","created_at":"2026-07-05T02:38:27Z","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":"608fd59091da9dd0e4dae147546a93f039ac4b5198e06617ea8048263f41f32d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T23:13:46Z","title_canon_sha256":"f5b75b37a212ab156ee8afc71b0b5567ff5f24c50f9716c1afd45c05bcb63c4d"},"schema_version":"1.0","source":{"id":"1907.11815","kind":"arxiv","version":1}},"canonical_sha256":"faf1ae4db8e30ef8ac595924ab3b2293e3cea7310673225499eaf9e96d0a5536","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"faf1ae4db8e30ef8ac595924ab3b2293e3cea7310673225499eaf9e96d0a5536","first_computed_at":"2026-07-05T02:38:27.311657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:38:27.311657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+CsmiqyxbsiW2nGF+nIMuIoE6PM4t0ttsmyBVbquH+d9jTSSYFMq+7bXjKPoE6ak1F0Ifx96WTXL2qMNe+FlDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:38:27.312173Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.11815","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb76b9b8e0851262dcbdc002a24bf0e5814de14b1d68a51e77dac22ea91130fa","sha256:7ce9ee653fd0484e6a6c3544acf3c32d88172c8a2c028344ad84904270584db4"],"state_sha256":"94d61c9ac97aa0e3b03c855f889a585742be7f7a103861013b7eb592b3748242"}