{"id":"W3039352388","doi":"","title":"Tslearn, A Machine Learning Toolkit for Time Series Data","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":422,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Python (programming language); MIT License; Cluster analysis; Header; Feature selection; Machine learning; Data mining; Artificial intelligence; Programming language; Software","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00251012,0.00242764,0.001517806,0.002455193,0.0007321425,0.002457918,0.00358404,0.001867425,0.03159998],"category_scores_gemma":[0.01806728,0.001665262,0.003093658,0.00291092,0.0004164476,0.004081296,0.003207831,0.004984974,0.02812261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006839064,"about_ca_system_score_gemma":0.002380247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007557795,"about_ca_topic_score_gemma":0.0153793,"domain_scores_codex":[0.9984842,0.0003763635,0.0003049315,0.0003394489,0.0004083253,0.00008685128],"domain_scores_gemma":[0.9946101,0.003479735,0.0001982305,0.0007993379,0.0006575697,0.0002549997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001042868,0.0004071857,0.002959432,0.003623191,0.000784826,0.0007063724,0.0004292156,0.0429544,0.006332092,0.01191318,0.4415392,0.487308],"study_design_scores_gemma":[0.0005298157,0.0001938644,0.001844739,0.0005104239,0.000302776,0.0004679218,0.0001180722,0.6605285,0.01591586,0.07256695,0.2467916,0.0002294077],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.003151564,0.001121803,0.6041752,0.0004168435,0.0005317866,0.0002913316,0.03268284,0.3542345,0.003394169],"genre_scores_gemma":[0.03945283,0.00196029,0.8050199,0.0006679633,0.0001975046,0.001448016,0.106377,0.02911525,0.01576123],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03159998,"threshold_uncertainty_score":0.1057124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02845174199172006,"score_gpt":0.2351703904673543,"score_spread":0.2067186484756342,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}