{"id":"W1892839219","doi":"10.1002/cem.2533","title":"Statistical properties of signal entropy for use in detecting changes in time series data","year":2013,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Entropy (arrow of time); Differential entropy; Time series; Sample entropy; Maximum entropy spectral estimation; White noise; Transfer entropy; Change detection; Series (stratigraphy); System identification; Algorithm; Data mining; Principle of maximum entropy; Artificial intelligence; Mathematics; Statistics; Machine learning; Measure (data warehouse)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004652571,0.00008042106,0.0002890621,0.0007184821,0.000009334931,0.00005582513,0.0001996536,0.00005844111,0.00002769956],"category_scores_gemma":[0.0008985971,0.0000661351,0.0000248269,0.0006153043,0.00001575833,0.0004152216,0.00002685386,0.0001582679,0.000002919468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006921468,"about_ca_system_score_gemma":0.00001744985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003321376,"about_ca_topic_score_gemma":0.00004409565,"domain_scores_codex":[0.9990821,0.00002419528,0.0004552152,0.00006597163,0.0002188225,0.0001536421],"domain_scores_gemma":[0.9994105,0.0001916642,0.0001266451,0.0001096214,0.0001156148,0.00004600158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003573574,0.000163176,0.01431695,0.001136706,0.0001467227,0.00002349912,0.0005686771,0.004586019,0.8710154,0.00002094265,0.001473523,0.1061911],"study_design_scores_gemma":[0.004534841,0.00071426,0.008440818,0.0006615215,0.00005129864,0.000162425,0.0009931029,0.6722149,0.3051442,0.00009470434,0.006525815,0.0004620722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942176,0.0006031038,0.004737527,0.0000795187,0.0001775113,0.0001303012,0.00002056973,0.00001360276,0.00002030105],"genre_scores_gemma":[0.9973635,0.00005888824,0.002383729,0.000008262185,0.00009159136,0.00000763094,0.000001354471,0.00001490348,0.00007013001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6676289,"threshold_uncertainty_score":0.2696911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04068541875529027,"score_gpt":0.2369569219565983,"score_spread":0.1962715032013081,"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."}}