{"id":"W4399828316","doi":"10.32920/26052709.v1","title":"Local Interpretability Methods for Time Series Modeling","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Unilever (Canada)","funders":"Mitacs","keywords":"Interpretability; Series (stratigraphy); Computer science; Time series; Artificial intelligence; Machine learning; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01550995,0.00186639,0.001333806,0.00422021,0.0006711387,0.003688973,0.001669768,0.001808128,0.005094474],"category_scores_gemma":[0.07520628,0.0005256479,0.003160216,0.00250986,0.002613941,0.005234596,0.002475611,0.004545147,0.0007736912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146507,"about_ca_system_score_gemma":0.001196971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400775,"about_ca_topic_score_gemma":0.002458666,"domain_scores_codex":[0.9895301,0.006706253,0.0006419634,0.001199902,0.001712387,0.0002093778],"domain_scores_gemma":[0.9340731,0.05582436,0.003513327,0.004007252,0.002130501,0.0004514959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002319139,0.0001596033,0.007042213,0.0007981021,0.0005431182,0.0004659659,0.001519139,0.3735822,0.002387,0.3582893,0.004755368,0.250226],"study_design_scores_gemma":[0.00001751085,0.0000661193,0.0009079845,0.0001049603,0.00004012151,0.0000570246,0.0001128337,0.783424,0.0006328808,0.2116678,0.002937251,0.0000315429],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004314257,0.0006286251,0.9925894,0.0004926656,0.00005781533,0.00006408155,0.0001306811,0.0002901837,0.001432393],"genre_scores_gemma":[0.4634053,0.001884836,0.5264141,0.0005493375,0.0008912683,0.000876951,0.001287297,0.0007603313,0.003930474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01550995,"threshold_uncertainty_score":0.08202541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098396244139448,"score_gpt":0.3318623341939894,"score_spread":0.3008783717525949,"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."}}