{"id":"W2977860868","doi":"10.1109/ijcnn.2019.8851792","title":"A Novel LSTM Approach for Asynchronous Multivariate Time Series Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Univariate; Pipeline (software); Asynchronous communication; Multivariate statistics; Interpolation (computer graphics); Artificial intelligence; Convergence (economics); Recurrent neural network; Time series; Series (stratigraphy); Representation (politics); Set (abstract data type); Encoding (memory); Artificial neural network; Pattern recognition (psychology); 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":[0.0003344402,0.0005554646,0.0003387638,0.0003044751,0.000228253,0.0003715988,0.0009666461,0.0005677533,0.002922023],"category_scores_gemma":[0.0009979018,0.0002416253,0.000421553,0.0005953097,0.0002315811,0.000970951,0.0006450726,0.001077147,0.0007097678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004385921,"about_ca_system_score_gemma":0.000755217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003493015,"about_ca_topic_score_gemma":0.005911192,"domain_scores_codex":[0.999824,0.00003184096,0.00001178598,0.00005946559,0.00005196435,0.00002097954],"domain_scores_gemma":[0.9998485,0.00004554004,0.00001958772,0.00002640173,0.00004814507,0.00001190361],"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":[0.0001399196,0.00009689372,0.0007336508,0.0001654691,0.00012275,0.0001744461,0.0001115047,0.3558659,0.05663852,0.02335165,0.0056885,0.5569109],"study_design_scores_gemma":[0.000003087189,0.00002348963,0.0001224032,0.000004620783,0.000008604386,0.00002237707,0.000004122268,0.9916637,0.003616417,0.003161215,0.001364633,0.000005368152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006880512,0.0001795106,0.99056,0.0001134691,0.00007887406,0.0000218861,0.0001287292,0.000849458,0.001187628],"genre_scores_gemma":[0.4460267,0.0004520461,0.5452534,0.0001976207,0.0001769975,0.0001521028,0.0005833826,0.0001831906,0.006974621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003493015,"threshold_uncertainty_score":0.009775162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274023726672859,"score_gpt":0.2212754795285829,"score_spread":0.2085352422618543,"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."}}