{"id":"W1979464351","doi":"10.1016/j.ijar.2014.11.002","title":"Using interval information granules to improve forecasting in fuzzy time series","year":2014,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":140,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Canada Research Chairs","keywords":"Interval (graph theory); Computer science; Series (stratigraphy); Partition (number theory); Time series; Fuzzy logic; Benchmark (surveying); Data mining; Domain of discourse; Basis (linear algebra); Generalization; Artificial intelligence; Algorithm; Mathematics; Machine learning; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0112367,0.0001927605,0.0004451447,0.001342282,0.00008278523,0.0006714137,0.001199438,0.00007937758,0.00004988825],"category_scores_gemma":[0.02833836,0.0001554915,0.0001836518,0.0005752083,0.00005417201,0.002574146,0.0003454017,0.0003098002,0.00003707384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002527028,"about_ca_system_score_gemma":0.000103018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003181053,"about_ca_topic_score_gemma":0.000007805786,"domain_scores_codex":[0.9956093,0.0003887328,0.001687968,0.0002167356,0.001793125,0.000304158],"domain_scores_gemma":[0.9952279,0.001374735,0.001452537,0.0002141481,0.001577681,0.000153046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001370441,0.00007438372,0.01921916,0.00002046928,0.0001127159,0.00005494107,0.00376378,0.02036539,0.006199307,0.007140518,0.0005435942,0.9411353],"study_design_scores_gemma":[0.002090007,0.0005080957,0.01075728,0.001514474,0.00003333388,0.002297195,0.00179414,0.8940044,0.005876789,0.07445858,0.006088614,0.00057707],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6957065,0.00001453086,0.2943136,0.000450845,0.001629745,0.0001362322,0.000009096341,0.00002632608,0.00771308],"genre_scores_gemma":[0.6192091,0.000001689339,0.3801799,0.0001390218,0.0003965148,0.000003118759,0.000001837831,0.00001542364,0.00005347995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9405583,"threshold_uncertainty_score":0.9798464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06884974176019318,"score_gpt":0.3731629320079009,"score_spread":0.3043131902477078,"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."}}