{"id":"W2963558246","doi":"10.1109/access.2019.2930069","title":"An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"National Research Foundation of Korea; National IT Industry Promotion Agency; National Research Foundation","keywords":"Computer science; Benchmark (surveying); Multivariate statistics; Context (archaeology); Artificial intelligence; Recurrent neural network; Time series; Artificial neural network; Machine learning; Selection (genetic algorithm); Series (stratigraphy); Variable (mathematics); End-to-end principle; Feature selection; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003685042,0.000337447,0.0005280778,0.0005861757,0.0003724681,0.0007645778,0.001371695,0.0001400515,0.0004742465],"category_scores_gemma":[0.001670339,0.0002412221,0.0001070619,0.001597668,0.00008093427,0.002515503,0.0001474654,0.0002149239,0.0001917898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001427975,"about_ca_system_score_gemma":0.0001759516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001612701,"about_ca_topic_score_gemma":0.0004423477,"domain_scores_codex":[0.9961371,0.0005010054,0.0006130722,0.001106381,0.001047971,0.000594468],"domain_scores_gemma":[0.9941891,0.003540379,0.0004930687,0.0006801793,0.0008747189,0.0002225432],"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":[0.009770228,0.000342105,0.05981433,0.00007333791,0.0003197978,0.00002573129,0.004591754,0.04620117,0.05449239,0.0009664518,0.002155574,0.8212472],"study_design_scores_gemma":[0.001676046,0.002539053,0.05190177,0.0001591075,0.00006888898,0.0001129089,0.000281345,0.8944501,0.02185644,0.0232235,0.002808673,0.0009221954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6621605,0.000004680333,0.333422,0.00009372038,0.0007553922,0.001063627,0.00004071955,0.0001296436,0.002329636],"genre_scores_gemma":[0.8257737,2.695811e-7,0.1701532,0.0001034541,0.0001952521,0.0001252918,0.000007582721,0.00005659964,0.003584706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8482489,"threshold_uncertainty_score":0.9836752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09419881479961732,"score_gpt":0.4004594227550289,"score_spread":0.3062606079554115,"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."}}