{"id":"W4387936458","doi":"10.23977/acss.2023.070816","title":"Simulation of Multidimensional Time Series Data Analysis Model Based on Deep Learning","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Residual; Series (stratigraphy); Convolution (computer science); Pyramid (geometry); Time series; Artificial intelligence; Algorithm; Feature (linguistics); Pattern recognition (psychology); Deep learning; Data mining; Data structure; Artificial neural network; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000773593,0.0001392799,0.0004137672,0.0004137789,0.0001083914,0.00008657992,0.0004137479,0.00004245253,0.00000326936],"category_scores_gemma":[0.00003663303,0.0001197723,0.00006721534,0.001347517,0.00003281816,0.000862421,0.0003182646,0.00008492872,0.000006286116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001103459,"about_ca_system_score_gemma":0.0000147298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001446352,"about_ca_topic_score_gemma":0.00000903843,"domain_scores_codex":[0.9983767,0.0001373991,0.000446804,0.0005100283,0.0003297437,0.0001993802],"domain_scores_gemma":[0.9985113,0.0006052235,0.0002269039,0.0005222762,0.00008678315,0.00004751023],"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":[0.00000879654,0.00001612819,0.001379263,0.00002721838,0.00003759472,0.00000473491,0.0001408331,0.9697528,0.00004605164,0.0005623774,0.000003860114,0.0280203],"study_design_scores_gemma":[0.0001542194,0.00008339704,0.0003421021,0.00007076313,0.00002532432,5.306629e-7,0.00001623739,0.9987884,0.00001015401,0.0001153299,0.0002622078,0.0001314061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008297481,0.0006667961,0.9906878,0.00003157365,0.00008165316,0.00008810819,0.000009734183,0.00007164125,0.00006516778],"genre_scores_gemma":[0.9606458,0.00005350128,0.03910052,0.0000200854,0.00004188695,0.000004082809,0.00006900305,0.000007134491,0.00005798271],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9523484,"threshold_uncertainty_score":0.4884173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059568012008897,"score_gpt":0.2817002907128094,"score_spread":0.2511046105927204,"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."}}