{"id":"W4376619788","doi":"10.1016/j.neucom.2023.126328","title":"Time series prediction with granular neural networks","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Artificial neural network; Computer science; Granularity; Generalization; Robustness (evolution); Time series; Series (stratigraphy); Vagueness; Interval (graph theory); Artificial intelligence; Data mining; Granular computing; Machine learning; Algorithm; Mathematics; Fuzzy logic; Rough set","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":[],"consensus_categories":[],"category_scores_codex":[0.0001151815,0.0001349193,0.0001213624,0.00006690336,0.0003649994,0.0001908015,0.0004606994,0.0000349859,0.000002688025],"category_scores_gemma":[0.000004513209,0.0001143956,0.00004045913,0.00117259,0.00004265566,0.0003894607,0.000241089,0.0001900478,0.00006619908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007526891,"about_ca_system_score_gemma":0.000009106892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002368456,"about_ca_topic_score_gemma":7.964002e-7,"domain_scores_codex":[0.9988315,0.00004395054,0.0001702851,0.0004066497,0.0001861684,0.000361501],"domain_scores_gemma":[0.9993609,0.00008407486,0.00007030509,0.0003648213,0.00003982452,0.00008012693],"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":[0.000006821819,0.00001959372,0.001490212,0.000007755651,0.000006736632,0.00006435214,0.000109769,0.9407396,0.0006087252,0.003174336,0.005969371,0.04780276],"study_design_scores_gemma":[0.0001222049,0.00008380185,0.007505678,0.0000112444,0.000003492357,0.00006829716,0.000003610651,0.9886014,0.00007122518,0.0001841184,0.003235506,0.0001094123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3613658,0.00003775878,0.62832,0.004292747,0.0005622508,0.000537615,0.000002659086,0.003994495,0.0008866754],"genre_scores_gemma":[0.9947535,0.000008307704,0.003916892,0.0004938066,0.0004556915,0.00002659541,0.00001356009,0.00002328251,0.0003084127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6333876,"threshold_uncertainty_score":0.4664919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00905265314911462,"score_gpt":0.2076042507202718,"score_spread":0.1985515975711572,"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."}}