{"id":"W3094606517","doi":"10.18280/ria.340412","title":"Power Customer Complaint Prediction Model Based on Time Series Analysis","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Time series; Artificial neural network; Autoregressive model; Backpropagation; Mean squared prediction error; Predictive power; Series (stratigraphy); Predictive modelling; Genetic algorithm; Data mining; Power (physics); Machine learning; Artificial intelligence; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006400187,0.0006775322,0.0005278956,0.0009329933,0.0003567167,0.0009432246,0.001002004,0.0006755331,0.00123956],"category_scores_gemma":[0.001794056,0.0002274052,0.0007874434,0.001071722,0.0002237037,0.001094606,0.0003549913,0.0006197501,0.0003740105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006664927,"about_ca_system_score_gemma":0.0006954017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01736255,"about_ca_topic_score_gemma":0.008546924,"domain_scores_codex":[0.9995604,0.00008072683,0.0000390885,0.0001177071,0.000148867,0.00005320464],"domain_scores_gemma":[0.9993652,0.0002260345,0.0001004083,0.00003615341,0.0002475056,0.00002470442],"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":[0.0001358301,0.0001718454,0.01570119,0.00008551667,0.00009614541,0.0003047843,0.0001399007,0.9086153,0.001822821,0.002896394,0.003132457,0.0668978],"study_design_scores_gemma":[0.000001789094,0.000009352485,0.0007477695,0.000001640992,0.000007458323,0.00001212145,0.000006929905,0.9986572,0.000152084,0.0002874116,0.0001128257,0.000003415936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4061975,0.0006004765,0.5792471,0.0008554354,0.0001792697,0.0001317218,0.000799416,0.002399281,0.009589969],"genre_scores_gemma":[0.9840263,0.0002373124,0.01274326,0.00004005552,0.00004052045,0.0000826079,0.0003798749,0.00002922383,0.0024208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01736255,"threshold_uncertainty_score":0.03452295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04728908287714888,"score_gpt":0.3008858257938272,"score_spread":0.2535967429166783,"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."}}