{"id":"W2896763107","doi":"10.20944/preprints201810.0494.v1","title":"Unsupervised Feature Learning in Time Series Prediction Using Continuous Deep Belief Network","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Convergence (economics); Feature (linguistics); Stability (learning theory); Process (computing); Series (stratigraphy); Machine learning; Time series; Artificial neural network; Deep belief network; Deep learning; Data mining; Pattern recognition (psychology)","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":[0.0007759554,0.000622251,0.0007301018,0.0004163948,0.0002650768,0.0007787535,0.001045617,0.0008171846,0.0006064574],"category_scores_gemma":[0.002542823,0.0003770665,0.000445015,0.0006205634,0.0005117574,0.001111019,0.0009006814,0.001466746,0.0001432444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005460799,"about_ca_system_score_gemma":0.0006947367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005015874,"about_ca_topic_score_gemma":0.003471911,"domain_scores_codex":[0.9996848,0.0000789095,0.0000196779,0.00009436163,0.00008357896,0.00003870474],"domain_scores_gemma":[0.9991436,0.0004405271,0.00009333277,0.00008577137,0.0001972793,0.00003943198],"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":[0.0001234425,0.00009066245,0.001734441,0.00008835422,0.00007081826,0.00008090083,0.00007156064,0.8421081,0.00538837,0.008500724,0.0007474319,0.1409952],"study_design_scores_gemma":[0.000002056594,0.000007039137,0.00007257995,0.000001519673,0.000002467322,0.000004164863,0.000001230833,0.9986659,0.000336827,0.0008366228,0.00006780556,0.000001674926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03196549,0.0005018004,0.9661723,0.0001384179,0.0000468775,0.00001631941,0.00004382923,0.000351641,0.000763313],"genre_scores_gemma":[0.8882189,0.0003326486,0.1097868,0.0000897547,0.00005225484,0.00006534403,0.000140848,0.00003735476,0.001275992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005015874,"threshold_uncertainty_score":0.009973347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04911092902151366,"score_gpt":0.291537111956616,"score_spread":0.2424261829351024,"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."}}