{"id":"W4404612815","doi":"10.1007/s10489-024-05856-6","title":"Unsupervised anomaly detection and imputation in noisy time series data for enhancing load forecasting","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Anomaly detection; Imputation (statistics); Time series; Data mining; Series (stratigraphy); Anomaly (physics); Artificial intelligence; Machine learning; Missing data","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":[],"consensus_categories":[],"category_scores_codex":[0.0003764541,0.0001421706,0.0001258204,0.00009784857,0.00006670614,0.0001016957,0.0001323728,0.0000724865,0.000009544897],"category_scores_gemma":[0.00006079168,0.0001507109,0.00001607776,0.0002648303,0.00002230747,0.0003494969,0.00005881026,0.0001487586,0.00001810195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006435119,"about_ca_system_score_gemma":0.00002180953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003164214,"about_ca_topic_score_gemma":0.0006022188,"domain_scores_codex":[0.9991299,0.000006770144,0.0002582199,0.0002968273,0.00008224989,0.0002260183],"domain_scores_gemma":[0.9995371,0.0002192741,0.00001585368,0.0001721862,0.0000190393,0.00003655279],"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":[0.00003487438,0.000006566235,0.00002816968,0.0006409906,0.00003497873,0.000007962958,0.002152976,0.07179295,0.1740399,0.001276983,0.00003136245,0.7499523],"study_design_scores_gemma":[0.00004148779,0.00002468366,0.00002241137,0.0001442521,0.0000109111,0.00001640362,0.0002111816,0.8743782,0.1223025,0.00130158,0.001373021,0.0001733837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2577553,0.001360372,0.7353471,0.00001556952,0.0004007316,0.0003295621,0.00003059925,0.0004813861,0.004279339],"genre_scores_gemma":[0.994148,0.00006881524,0.005478783,0.000008376419,0.0001240524,0.0000478914,0.0000454335,0.00003735152,0.00004136644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8025852,"threshold_uncertainty_score":0.614581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285910828892971,"score_gpt":0.2345619835304911,"score_spread":0.2117028752415614,"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."}}