{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001483735,0.000537533,0.001245306,0.0008500285,0.0004216098,0.001594624,0.0008688395,0.0009280334,0.001653209],"category_scores_gemma":[0.006510056,0.000525764,0.0006978289,0.001045408,0.0006225191,0.002053891,0.00126117,0.001556477,0.0002821438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007559778,"about_ca_system_score_gemma":0.0004929427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007417791,"about_ca_topic_score_gemma":0.004527735,"domain_scores_codex":[0.999598,0.00009258152,0.00005120493,0.0001004069,0.00009151522,0.00006636403],"domain_scores_gemma":[0.9976811,0.001327192,0.0002614531,0.0003485073,0.0002564211,0.0001253283],"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":[0.0006142575,0.0001350277,0.003946823,0.0001186501,0.0001662299,0.0002133069,0.00005363249,0.8677829,0.001695529,0.01743054,0.001832331,0.1060108],"study_design_scores_gemma":[0.000005128537,0.000007867977,0.0001560674,0.000003973153,0.000005327264,0.000004575536,0.000002338038,0.9950188,0.0001068823,0.00463508,0.00005153343,0.000002401138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101029,0.001914722,0.7811783,0.0007538479,0.0005945336,0.00008225179,0.0003912284,0.00151475,0.003467585],"genre_scores_gemma":[0.9523705,0.0003854401,0.04536122,0.00007734096,0.0001127371,0.00004566874,0.0002268398,0.00003796217,0.001382298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007417791,"threshold_uncertainty_score":0.01474923,"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."}}