{"id":"W4320509455","doi":"10.2991/978-94-6463-036-7_137","title":"Comparison of SVM and ARIMA Model in Stock Market","year":2022,"lang":"en","type":"book-chapter","venue":"Advances in economics, business and management research/Advances in Economics, Business and Management Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Autoregressive integrated moving average; Support vector machine; Econometrics; Stock (firearms); Stock market; Computer science; Time series; Economics; Financial economics; Artificial intelligence; Machine learning; Engineering; Geography","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0244396,0.001006914,0.002634539,0.009534669,0.000590317,0.0007924125,0.002562692,0.0003707553,0.0003705258],"category_scores_gemma":[0.001007265,0.00107613,0.0001337567,0.001657098,0.002611132,0.003504314,0.009103279,0.001782844,0.000009744751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135695,"about_ca_system_score_gemma":0.0001988204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004616983,"about_ca_topic_score_gemma":0.009738378,"domain_scores_codex":[0.9886402,0.0007616702,0.003546087,0.00371398,0.001370872,0.001967208],"domain_scores_gemma":[0.9924915,0.003396709,0.001158768,0.001919118,0.0007343728,0.0002995042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001003113,0.0002455739,0.01635437,0.00215668,0.00008353758,0.00008264557,0.0001918186,0.04146272,4.275322e-7,0.2653603,0.000565609,0.6724933],"study_design_scores_gemma":[0.002474287,0.0001323041,0.02913512,0.0007780238,0.00002789209,0.000007149549,0.001537879,0.05089587,0.000001610941,0.3871273,0.5269073,0.0009753233],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.03176956,0.05587224,0.002114784,0.002061814,0.001441769,0.007134588,0.000237649,0.00005570913,0.8993119],"genre_scores_gemma":[0.04714848,0.8841088,0.009581433,0.00008160724,0.0001377065,0.0009345099,0.00006754115,0.0001975753,0.05774229],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8415696,"threshold_uncertainty_score":0.9991689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1968386125540462,"score_gpt":0.4588522248271161,"score_spread":0.2620136122730699,"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."}}