{"id":"W3197278397","doi":"","title":"NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Simon Fraser University","funders":"","keywords":"Directed acyclic graph; Conditional independence; Computer science; Nonparametric statistics; Independence (probability theory); Constraint (computer-aided design); Series (stratigraphy); Graph; Set (abstract data type); Time series; Artificial intelligence; Algorithm; Machine learning; Data mining; Theoretical computer science; Mathematics; Econometrics; Statistics","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":[0.003911008,0.00133227,0.00129166,0.001974596,0.000569038,0.001538993,0.002574326,0.001248647,0.004208907],"category_scores_gemma":[0.01974213,0.0007670164,0.00154604,0.002208085,0.000754731,0.003077328,0.002156845,0.002997843,0.001307466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217696,"about_ca_system_score_gemma":0.002664797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008676491,"about_ca_topic_score_gemma":0.01721182,"domain_scores_codex":[0.9987423,0.0006014575,0.00007562317,0.0003132157,0.0001795209,0.00008792149],"domain_scores_gemma":[0.9938396,0.003957486,0.0005557647,0.0008446245,0.0004932195,0.0003093591],"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":[0.0002888417,0.0002470812,0.007950559,0.0002436131,0.0003554101,0.000157041,0.000132328,0.6782126,0.001550885,0.05778788,0.01160759,0.2414661],"study_design_scores_gemma":[0.00001678657,0.0000267702,0.0004988953,0.00001078003,0.00001338051,0.00001865159,0.00001389446,0.9624732,0.000296763,0.03518639,0.001432166,0.00001240861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009442139,0.00019412,0.9870227,0.0002618236,0.00006662674,0.00007141214,0.001120838,0.001174826,0.0006454586],"genre_scores_gemma":[0.4044862,0.000646921,0.5781357,0.000335187,0.0002450967,0.0004754051,0.009663394,0.0005796587,0.005432384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008676491,"threshold_uncertainty_score":0.02068359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05900913503199905,"score_gpt":0.193790366126651,"score_spread":0.134781231094652,"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."}}