{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001538261,0.0003218862,0.0005755031,0.0001291743,0.0001011547,0.0007073987,0.001226842,0.0002315326,0.0001166522],"category_scores_gemma":[0.0001062526,0.0002597633,0.0006051714,0.0002119892,0.00006499384,0.0002219861,0.006018547,0.0004962115,0.00006357492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009764329,"about_ca_system_score_gemma":0.0001609795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009045602,"about_ca_topic_score_gemma":0.00001387921,"domain_scores_codex":[0.9977595,0.0001149822,0.0005665437,0.001071678,0.0001480218,0.0003392591],"domain_scores_gemma":[0.99837,0.0001588959,0.00009973143,0.001075559,0.0002000277,0.00009577363],"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":[0.0000199442,0.00002835556,0.000001335762,0.0005839683,0.0003100472,0.000002296784,0.001009902,0.1735341,0.0001313697,0.07819009,0.0004752806,0.7457133],"study_design_scores_gemma":[0.00002255927,0.00003050908,1.751085e-7,0.00007292995,0.00005466686,0.000004128515,0.00003905448,0.7800986,0.0004014926,0.2176976,0.001342494,0.0002357642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001347873,0.0007973933,0.9903696,0.001030126,0.0007145985,0.0002943743,0.00001237225,0.0005137118,0.006133078],"genre_scores_gemma":[0.04687018,0.000008433554,0.9497413,0.00007687293,0.0001053533,0.0000846518,0.00001537969,0.00002612079,0.003071694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7454776,"threshold_uncertainty_score":0.9999855,"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."}}