{"id":"W4388100099","doi":"10.36548/jscp.2023.3.007","title":"Fuel Sales Forecasting with SARIMA-GARCH and Rolling Window","year":2023,"lang":"en","type":"article","venue":"Journal of Soft Computing Paradigm","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mean absolute percentage error; Autoregressive conditional heteroskedasticity; Mean squared error; Econometrics; Term (time); Autoregressive integrated moving average; Statistics; Variance (accounting); Sample (material); Moving average; Computer science; Economics; Time series; Mathematics; Volatility (finance)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001031302,0.0005586958,0.0007353798,0.0008997165,0.000371805,0.0007898035,0.0008549861,0.0004015953,0.0009926553],"category_scores_gemma":[0.003231425,0.0003101057,0.0006505551,0.001283356,0.0001800364,0.001059717,0.0003578175,0.0007358947,0.0002444504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000743559,"about_ca_system_score_gemma":0.001688499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08417919,"about_ca_topic_score_gemma":0.06870793,"domain_scores_codex":[0.9995112,0.0001004231,0.0000329147,0.0001031036,0.0001841816,0.00006807836],"domain_scores_gemma":[0.9994342,0.0002233207,0.00007871063,0.00006902454,0.0001744366,0.00002033628],"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":[0.0001840393,0.00008996059,0.01875089,0.0001130478,0.0002477055,0.0002202117,0.0001393572,0.7559955,0.003728951,0.006633329,0.002882306,0.2110148],"study_design_scores_gemma":[0.000004769623,0.00001984767,0.002920152,0.000003881442,0.00001844485,0.00001563517,0.00001395578,0.9946852,0.0006690816,0.001068828,0.0005695182,0.00001066518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3826703,0.002354701,0.6047174,0.0007368152,0.0002215247,0.00007881888,0.0008757962,0.002155184,0.00618953],"genre_scores_gemma":[0.9504417,0.0006856172,0.04629162,0.00005222024,0.00006550311,0.00002377357,0.0005823776,0.00004587828,0.001811405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08417919,"threshold_uncertainty_score":0.1673784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04849548533764557,"score_gpt":0.2379490608984641,"score_spread":0.1894535755608185,"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."}}