{"id":"W1524579037","doi":"","title":"Comparison of Adaptive Neuro-fuzzy and Particle Swarm Optimization based Neural Network Models for Financial Time Series Prediction","year":2009,"lang":"en","type":"article","venue":"ASAC","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Adaptive neuro fuzzy inference system; Particle swarm optimization; Computer science; Time series; Neuro-fuzzy; Artificial intelligence; Machine learning; Series (stratigraphy); Data mining; Fuzzy logic; Fuzzy control system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001665425,0.0005772287,0.0007854032,0.0007216078,0.0002968147,0.0009126081,0.0009713055,0.001021795,0.001018788],"category_scores_gemma":[0.003674226,0.0002302844,0.0005396666,0.000642443,0.0003258379,0.0009811419,0.0003995407,0.0007209679,0.0001645285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007298083,"about_ca_system_score_gemma":0.0006769216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281003,"about_ca_topic_score_gemma":0.00864638,"domain_scores_codex":[0.9995787,0.0001844936,0.00002876914,0.00004465525,0.0001390468,0.00002440097],"domain_scores_gemma":[0.9986269,0.0009335602,0.00006851539,0.00005267826,0.0002903921,0.00002795913],"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":[0.0002232999,0.0001035715,0.001739965,0.00008036972,0.0000763018,0.00004645245,0.00004947763,0.9479448,0.0004799904,0.003298993,0.0004316359,0.04552517],"study_design_scores_gemma":[0.000006501133,0.00002314279,0.0002554811,0.000002542832,0.000005723851,0.000003267706,0.000003748313,0.9992343,0.00008910354,0.0002874655,0.0000862732,0.000002496457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.471038,0.004381045,0.5019805,0.001215345,0.000344179,0.0002578316,0.0001885169,0.0005342955,0.02006035],"genre_scores_gemma":[0.954504,0.0009995666,0.04181988,0.00006382205,0.00004884963,0.0001125182,0.00008561414,0.00002399488,0.002341786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01281003,"threshold_uncertainty_score":0.02547091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1198448839990486,"score_gpt":0.3754715345297198,"score_spread":0.2556266505306712,"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."}}