{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004888875,0.0004878755,0.0004449147,0.0004833049,0.0004890987,0.0006766281,0.0008248794,0.0008107024,0.003097268],"category_scores_gemma":[0.001001317,0.0002999244,0.0007462789,0.0004554935,0.0004269087,0.001129243,0.0005659029,0.000941277,0.0002256428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008385972,"about_ca_system_score_gemma":0.001003447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02761247,"about_ca_topic_score_gemma":0.01150691,"domain_scores_codex":[0.9998204,0.00003696432,0.00001478134,0.00004825114,0.00004536749,0.00003419496],"domain_scores_gemma":[0.9997169,0.000131605,0.00003073003,0.0000176294,0.00008392495,0.00001913266],"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":[0.00003589607,0.00001886103,0.001503483,0.00002429049,0.00001474687,0.0000524438,0.00003209159,0.9882175,0.0008588345,0.004236782,0.000348152,0.00465695],"study_design_scores_gemma":[0.00000132018,0.000002605157,0.00007111299,7.970157e-7,0.000001121657,0.00000256095,0.000001701581,0.9994274,0.00009776302,0.000356166,0.00003634425,0.000001114037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2576718,0.0005600394,0.7248366,0.001021609,0.0001419444,0.00009024322,0.0005615412,0.00126018,0.01385602],"genre_scores_gemma":[0.9771105,0.0002617502,0.01888789,0.00005547357,0.00001431595,0.0001242918,0.0002330232,0.00004121145,0.003271646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02761247,"threshold_uncertainty_score":0.05490351,"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."}}