{"id":"W4292959236","doi":"10.5267/j.ijdns.2022.6.004","title":"The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Padjadjaran","keywords":"Autoregressive integrated moving average; Univariate; Autoregressive model; Heteroscedasticity; Time series; Multivariate statistics; Moving average; Moving-average model; Variance (accounting); Statistics; Box–Jenkins; Mean absolute percentage error; Computer science; Econometrics; Data mining; Mathematics; Mean squared error","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":[0.001195432,0.0004227965,0.0005164748,0.000945565,0.0005151962,0.0008814562,0.00066835,0.0005162071,0.0007740009],"category_scores_gemma":[0.002788092,0.0002581915,0.0007715968,0.00153922,0.0001505772,0.000959427,0.000424538,0.0007524961,0.0002098545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006641314,"about_ca_system_score_gemma":0.001450055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03769635,"about_ca_topic_score_gemma":0.02858618,"domain_scores_codex":[0.9993086,0.0002201728,0.00007542685,0.000151141,0.000179459,0.00006530596],"domain_scores_gemma":[0.9993068,0.000352445,0.0000736428,0.00004896796,0.0001944816,0.00002361576],"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":[0.0002770232,0.0004778746,0.1157288,0.0004303461,0.000504478,0.0005912805,0.0009643073,0.5284473,0.006401393,0.009880371,0.004671708,0.331625],"study_design_scores_gemma":[0.00002082329,0.00008537093,0.01516014,0.00002786919,0.00008122608,0.00006220394,0.0002682725,0.9771981,0.00251356,0.002069735,0.002476115,0.0000364976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5297601,0.0009749554,0.458967,0.001067766,0.0001487762,0.0002204503,0.001130253,0.001962312,0.005768418],"genre_scores_gemma":[0.854749,0.000684044,0.1404219,0.00007862234,0.00003504308,0.0001863784,0.001220035,0.00007094799,0.002553989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03769635,"threshold_uncertainty_score":0.07495391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1587099163189113,"score_gpt":0.4184433805244925,"score_spread":0.2597334642055813,"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."}}