{"id":"W1892337079","doi":"10.3233/ida-2012-00563","title":"Periodic pattern analysis of non-uniformly sampled stock market data","year":2012,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Stock market; Data mining; Time series; Missing data; Transaction data; Database transaction; Algorithm; Biological data; Machine learning","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002523353,0.0003664081,0.001283869,0.001993945,0.0002153295,0.0003466771,0.008771246,0.0001052696,0.002571162],"category_scores_gemma":[0.0002315617,0.0003154902,0.0006895263,0.01083815,0.0001101076,0.002520852,0.00592824,0.0002034161,0.00006856088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005690373,"about_ca_system_score_gemma":0.00006354882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276145,"about_ca_topic_score_gemma":0.002512546,"domain_scores_codex":[0.9957244,0.0001768647,0.001221614,0.001251431,0.0008813865,0.0007442402],"domain_scores_gemma":[0.9880568,0.0002833493,0.00071684,0.01040531,0.0002107664,0.0003269301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002207014,0.0005144166,0.5071611,0.00004972787,0.05967485,0.0000103608,0.001528898,0.004097367,0.00008616777,0.0002168451,0.005588723,0.4210495],"study_design_scores_gemma":[0.00006264164,0.00002409758,0.05609058,0.000007584461,0.01947265,0.000001282115,0.000235603,0.913889,0.00007469126,0.000008930255,0.009798174,0.0003347588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01223882,0.0006757615,0.9840144,0.0001273895,0.0001219516,0.0001157455,0.002040192,0.00005756687,0.0006081721],"genre_scores_gemma":[0.9649093,0.0002613805,0.02576625,0.0001287219,0.0001134147,0.000005447344,0.008514727,0.0000180192,0.0002827015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9582481,"threshold_uncertainty_score":0.9999297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08593796217694967,"score_gpt":0.3167714887996536,"score_spread":0.2308335266227039,"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."}}