{"id":"W4408441646","doi":"10.3390/computation13030076","title":"Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Computation","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Exponential smoothing; Random forest; Econometrics; Decision tree; Leverage (statistics); Computer science; Gradient boosting; Linear regression; Autoregressive integrated moving average; Univariate; Machine learning; Artificial intelligence; Economics; Time series","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008995182,0.0001172535,0.0002043236,0.0001379362,0.0003452369,0.0001345463,0.0003127038,0.00006922844,0.000009636723],"category_scores_gemma":[0.001260528,0.00009794838,0.00005311257,0.000440821,0.00007346975,0.0001971799,0.0001278979,0.0001475178,0.000008298889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005065327,"about_ca_system_score_gemma":0.00007171839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178747,"about_ca_topic_score_gemma":0.00006581937,"domain_scores_codex":[0.9983291,0.0001409121,0.0004610867,0.0004993769,0.0004343887,0.0001351209],"domain_scores_gemma":[0.9959969,0.003242839,0.0002193698,0.0002138861,0.0002974402,0.00002957894],"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":[0.0001181657,0.00003901778,0.0005170197,0.000007989664,0.00001442217,2.60875e-7,0.0002888741,0.42108,0.00001881583,0.05052984,0.001231846,0.5261537],"study_design_scores_gemma":[0.0001570922,0.00005648812,0.00474587,0.00002119953,0.00001125282,1.525567e-7,0.00002525785,0.5268764,0.00001945486,0.467716,0.0003213549,0.00004944779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03541378,0.0001488863,0.9615297,0.0004407489,0.00006963185,0.0006241042,0.0001110369,0.0001279022,0.001534223],"genre_scores_gemma":[0.7426268,0.000001823278,0.2567671,0.00004334036,0.00003551065,0.0001682339,0.0001635084,0.000007791313,0.0001858412],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.707213,"threshold_uncertainty_score":0.3994219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2858530263906696,"score_gpt":0.4249391600912907,"score_spread":0.1390861337006211,"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."}}