{"id":"W3134631187","doi":"10.3390/su13052761","title":"An Effective Hybrid Approach for Forecasting Currency Exchange Rates","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exponential smoothing; Autoregressive integrated moving average; Mean absolute percentage error; Mean squared error; Foreign exchange market; Exchange rate; Econometrics; Moving average; Support vector machine; Random walk; Economics; Computer science; Statistics; Time series; Mathematics; Finance; Machine learning","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":[0.0006687785,0.0006889301,0.0007441548,0.0007751912,0.0002271978,0.000758802,0.0006837125,0.0007097539,0.0008948693],"category_scores_gemma":[0.001037467,0.00034384,0.0007269338,0.0005860337,0.0001903158,0.0006704051,0.0003859777,0.0004046128,0.0001834201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002330449,"about_ca_system_score_gemma":0.0004349806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005123058,"about_ca_topic_score_gemma":0.004353006,"domain_scores_codex":[0.9997609,0.00005310448,0.00002160786,0.00005783104,0.0000788466,0.00002767047],"domain_scores_gemma":[0.999757,0.0001059717,0.0000346198,0.00002126793,0.0000686858,0.00001259169],"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":[0.00007487785,0.00004611006,0.001829302,0.00004613258,0.00009035131,0.00007626882,0.00003246099,0.8817757,0.003229285,0.002048032,0.0005641811,0.1101873],"study_design_scores_gemma":[0.000002502967,0.00001615705,0.0001463076,0.00000146053,0.000005012152,0.000007201801,0.000002224657,0.9993407,0.0001576834,0.0002106098,0.00010786,0.000002186838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06385916,0.0005064891,0.9323365,0.0001252078,0.00008729586,0.00003753882,0.0000753425,0.0006163425,0.002356105],"genre_scores_gemma":[0.8438215,0.000303703,0.1530957,0.00009363216,0.00006832496,0.0000773801,0.0002136191,0.00003635812,0.002289755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005123058,"threshold_uncertainty_score":0.01018643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161914337501394,"score_gpt":0.4440631469366597,"score_spread":0.3278717131865203,"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."}}