{"id":"W4386257024","doi":"10.32920/24050757","title":"Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Anomaly detection; Artificial immune system; Data mining; Constant false alarm rate; Outlier; Computation; CURE data clustering algorithm; Stock market; Kernel density estimation; False alarm; Artificial intelligence; Fuzzy clustering; Algorithm; Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111573,0.0004197305,0.0006909065,0.0008621789,0.0003233585,0.0009526246,0.001140212,0.0009574842,0.0008977643],"category_scores_gemma":[0.00230223,0.000219299,0.0007449694,0.0007379361,0.0005039253,0.0008037871,0.0007131487,0.0007469452,0.0002018134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006635354,"about_ca_system_score_gemma":0.0006133412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005325558,"about_ca_topic_score_gemma":0.002817851,"domain_scores_codex":[0.9995684,0.0001553276,0.00002033091,0.00009575659,0.000101571,0.00005862204],"domain_scores_gemma":[0.9991812,0.0004102961,0.00009697694,0.0000456571,0.000227984,0.00003788104],"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":[0.0001417081,0.000139438,0.005037731,0.00004638599,0.0001715355,0.0001128531,0.00009264507,0.9201622,0.002678151,0.0106272,0.001047095,0.05974318],"study_design_scores_gemma":[0.000001921125,0.0000119596,0.0001805967,0.000001216697,0.000006057705,0.000007411608,0.000003820896,0.9986274,0.0001521237,0.0009285758,0.00007655747,0.000002401646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2058872,0.000499242,0.786494,0.0009218677,0.0001267901,0.00008357281,0.00008669818,0.0005927034,0.005307925],"genre_scores_gemma":[0.9544949,0.0001649255,0.04228744,0.0001156459,0.0000547675,0.00004492335,0.00006748922,0.00001877487,0.002751043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005325558,"threshold_uncertainty_score":0.01058912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05986938991240406,"score_gpt":0.2287424760184544,"score_spread":0.1688730861060504,"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."}}