{"id":"W2064427830","doi":"10.4018/ijfsa.2013010101","title":"Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting","year":2013,"lang":"en","type":"article","venue":"International Journal of Fuzzy System Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial neural network; Computer science; Adaptive neuro fuzzy inference system; Fuzzy logic; Neuro-fuzzy; Demand forecasting; Artificial intelligence; Machine learning; Fuzzy control system; Operations research; Engineering","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":[0.002345846,0.0006497868,0.0006755621,0.001124989,0.0004391655,0.001112756,0.0005992428,0.000790204,0.001278053],"category_scores_gemma":[0.005456658,0.0002576512,0.0004795307,0.0009449395,0.0002941065,0.001129195,0.000447544,0.000520794,0.0001303721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001560463,"about_ca_system_score_gemma":0.0007886258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02207889,"about_ca_topic_score_gemma":0.01566697,"domain_scores_codex":[0.9994583,0.0002626815,0.00003252762,0.00005055104,0.0001511455,0.00004475867],"domain_scores_gemma":[0.9968195,0.002617323,0.0001288654,0.00005739267,0.0003407963,0.00003617622],"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":[0.000228954,0.0000681442,0.003004227,0.00007810268,0.00008880232,0.00007108204,0.00004513697,0.9517248,0.0003155118,0.005792652,0.0003881294,0.03819445],"study_design_scores_gemma":[0.0000025796,0.00001880469,0.0005162025,0.000005022034,0.000009572191,0.000005061432,0.000009404656,0.998599,0.00007964972,0.0006480205,0.0001029406,0.000003824506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6420997,0.01026888,0.3130066,0.001834593,0.0003619878,0.0001424423,0.0002690236,0.0004250373,0.03159166],"genre_scores_gemma":[0.9853667,0.001018869,0.01194373,0.00002776466,0.00004473297,0.0000347144,0.0000629536,0.00001370369,0.001486825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02207889,"threshold_uncertainty_score":0.04390079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1665803588915949,"score_gpt":0.4140372890400323,"score_spread":0.2474569301484374,"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."}}