{"id":"W1482270157","doi":"10.48550/arxiv.1210.4903","title":"Detecting Change-Points in Time Series by Maximum Mean Discrepancy of Ordinal Pattern Distributions","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Series (stratigraphy); Time series; Computer science; Consistency (knowledge bases); Monotonic function; Ordinal optimization; Algorithm; Calibration; Ordinal data; Pattern recognition (psychology); Statistics; Mathematics; Data mining; Artificial intelligence; Machine learning","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":[0.005819339,0.0007001918,0.001294159,0.003273606,0.0005075241,0.001748288,0.001629476,0.001308796,0.001141898],"category_scores_gemma":[0.02951889,0.0004569989,0.001065208,0.002806528,0.001599437,0.002935132,0.002219446,0.001904156,0.0003682093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006426506,"about_ca_system_score_gemma":0.0005348623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008031609,"about_ca_topic_score_gemma":0.000695313,"domain_scores_codex":[0.9977815,0.0008764082,0.0001991632,0.0005357034,0.0004848191,0.0001224733],"domain_scores_gemma":[0.9800822,0.01448387,0.002555439,0.001509983,0.0009726618,0.0003959117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009770909,0.0002799733,0.08153949,0.0005941264,0.000629325,0.0009116231,0.001045286,0.4629931,0.02695295,0.06660597,0.003182521,0.3542884],"study_design_scores_gemma":[0.00001919388,0.00007446221,0.01116214,0.00002939045,0.00001945858,0.0002457264,0.00007403671,0.9390053,0.002565633,0.04574445,0.00100823,0.00005189221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06031512,0.0003026142,0.9380625,0.000196137,0.00003655709,0.00003086812,0.0001657365,0.0002502005,0.0006401915],"genre_scores_gemma":[0.7681158,0.0003117099,0.2295615,0.0001029514,0.000130306,0.0001504595,0.0005994955,0.000159145,0.0008686149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005819339,"threshold_uncertainty_score":0.03077596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06115794595364967,"score_gpt":0.1659770723882295,"score_spread":0.1048191264345799,"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."}}