{"id":"W2946709941","doi":"10.48550/arxiv.1905.10437","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Computer science; Benchmark (surveying); Residual; Deep learning; Artificial intelligence; Univariate; Series (stratigraphy); Artificial neural network; Time series; Domain (mathematical analysis); Range (aeronautics); Machine learning; Architecture; Data mining; Algorithm; Multivariate statistics; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001238703,0.000367235,0.0007787829,0.0008948378,0.000319693,0.0003351097,0.001852992,0.0003338858,0.0003917926],"category_scores_gemma":[0.000711525,0.0003470927,0.000923263,0.00218176,0.0001435558,0.0004449761,0.001611035,0.0003757831,0.0001641756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001494403,"about_ca_system_score_gemma":0.0001144673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001336332,"about_ca_topic_score_gemma":0.00006716055,"domain_scores_codex":[0.9970704,0.0001379365,0.0005665973,0.001554219,0.0002496928,0.0004211394],"domain_scores_gemma":[0.9956699,0.001021544,0.000709825,0.001804448,0.0006478886,0.000146393],"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":[0.00025509,0.0001084799,0.02252717,0.00006644366,0.0005588512,0.00002275438,0.0003135521,0.9534417,0.0001778586,0.009326108,0.008553643,0.004648301],"study_design_scores_gemma":[0.0001544675,0.00008164968,0.0004865378,0.00005365121,0.0005316049,0.000002671378,0.0001757599,0.9451016,0.0002340399,0.05108009,0.001721355,0.0003765771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4024565,0.00002253225,0.5929548,0.0001635533,0.0002045986,0.0007379899,0.0002900141,0.0002448076,0.002925243],"genre_scores_gemma":[0.9751672,0.00001479309,0.007392891,0.00006314216,0.00006777201,0.00001146364,0.0001259895,0.00003115104,0.01712563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5855619,"threshold_uncertainty_score":0.9998981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2311203377402559,"score_gpt":0.2741654235903008,"score_spread":0.04304508585004496,"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."}}