{"id":"W2963912395","doi":"10.1016/j.patcog.2019.106973","title":"Learning representations of multivariate time series with missing data","year":2019,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Norges Forskningsråd","keywords":"Missing data; Autoencoder; Computer science; Dimensionality reduction; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Pairwise comparison; Time series; Machine learning; Deep learning; Algorithm; Data mining","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":[0.0001964581,0.00007382072,0.0001299321,0.00006255259,0.00007788753,0.00009371193,0.0003076152,0.00002232221,0.000209497],"category_scores_gemma":[0.0000362276,0.0000631334,0.00002475947,0.0002112967,0.00001875706,0.00104918,0.0002038478,0.00007992232,0.0001745743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006600308,"about_ca_system_score_gemma":0.00001588497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000122777,"about_ca_topic_score_gemma":0.00001167549,"domain_scores_codex":[0.9991891,0.0000627414,0.000180119,0.0002990524,0.0001493199,0.0001197086],"domain_scores_gemma":[0.9991824,0.00006452545,0.0001915022,0.0004371866,0.0000963777,0.00002799011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001800116,0.00004434568,0.02046635,0.00005345243,0.00008309528,0.000005909074,0.00107819,0.0006818154,0.01106569,0.00002437041,0.00003625109,0.9664425],"study_design_scores_gemma":[0.0006797455,0.0003450602,0.02158596,0.0003551116,0.00007215845,0.00005133597,0.0004019891,0.9653257,0.009589463,0.0005249027,0.0006975302,0.0003710515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4054623,0.00002187653,0.5885203,0.0004508222,0.00008845166,0.0001830908,0.00002716933,0.0001269123,0.005119104],"genre_scores_gemma":[0.9551089,0.000003487226,0.04391846,0.00002934133,0.00003329205,0.00000271929,0.0002881902,0.00001008556,0.0006055217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9660715,"threshold_uncertainty_score":0.2574505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03434366004606687,"score_gpt":0.2588712664701866,"score_spread":0.2245276064241197,"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."}}