{"id":"W4402305894","doi":"10.1016/j.asoc.2024.112195","title":"Oriented to a multi-learning mode: Establishing trend-fuzzy-granule-based LSTM neural networks for time series forecasting","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Time series; Artificial neural network; Artificial intelligence; Series (stratigraphy); Mode (computer interface); Fuzzy logic; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257291,0.0005443305,0.0004333867,0.000442594,0.0003002895,0.0009006152,0.0008992489,0.0009622845,0.001142612],"category_scores_gemma":[0.003122016,0.0003134841,0.0004833162,0.0006484461,0.0004460274,0.002097651,0.001014271,0.001137135,0.0003588623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003849288,"about_ca_system_score_gemma":0.0006193293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002516251,"about_ca_topic_score_gemma":0.002188676,"domain_scores_codex":[0.9997274,0.00006541276,0.00002746271,0.00009172464,0.00005801415,0.00002998081],"domain_scores_gemma":[0.9994808,0.0001562116,0.00005084664,0.00006804675,0.0002184616,0.00002563713],"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":[0.000249921,0.0002021025,0.004506589,0.0002173865,0.0001818873,0.0002270807,0.0003954455,0.4139433,0.0407472,0.04390834,0.002786546,0.4926342],"study_design_scores_gemma":[0.000002270599,0.00002225691,0.0001957468,0.000006246015,0.00001143555,0.00001618742,0.00000951025,0.9930647,0.002349342,0.004052464,0.0002648844,0.000005030597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03721558,0.0002773654,0.9605314,0.0001498093,0.00007030346,0.00004298605,0.00004440162,0.0002974428,0.001370675],"genre_scores_gemma":[0.7978987,0.0004138711,0.1990926,0.00009360058,0.00009799233,0.0001001097,0.0001350459,0.00006392356,0.002104155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002516251,"threshold_uncertainty_score":0.006649256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07456108232830093,"score_gpt":0.363110689323318,"score_spread":0.2885496069950171,"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."}}