{"id":"W3210195101","doi":"10.1109/isie45552.2021.9576224","title":"Inverted Dirichlet State Space Model for Time Series Forecasting","year":2021,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Inference; Dirichlet distribution; Series (stratigraphy); Latent Dirichlet allocation; Applied mathematics; Time series; State space; State-space representation; Latent variable; Computer science; Mathematics; Mathematical optimization; Algorithm; Topic model; Artificial intelligence; Statistics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003507266,0.001148655,0.002227384,0.001835301,0.0007808919,0.002174034,0.003053324,0.002120304,0.005251934],"category_scores_gemma":[0.01128415,0.0009459096,0.001885221,0.002528387,0.001733371,0.004616121,0.00141808,0.003966691,0.001483224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002114651,"about_ca_system_score_gemma":0.001371888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01549807,"about_ca_topic_score_gemma":0.01229247,"domain_scores_codex":[0.9980111,0.0008306275,0.00009106877,0.0005903702,0.0003044108,0.0001724194],"domain_scores_gemma":[0.9956872,0.003319816,0.0002729779,0.0002530908,0.0003830043,0.00008381071],"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":[0.0001189212,0.0000500094,0.001359679,0.00008397168,0.00009083808,0.0001072171,0.0001936084,0.8355256,0.000545891,0.1298552,0.002166687,0.02990231],"study_design_scores_gemma":[0.000005167334,0.000005531862,0.0001011244,0.0000068546,0.00000699513,0.00001071279,0.000008661541,0.9576237,0.00009958886,0.04154512,0.0005764275,0.000010193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01289665,0.0006058956,0.9832671,0.0005367682,0.0001021855,0.00003621271,0.0005036024,0.0003285258,0.00172311],"genre_scores_gemma":[0.7741925,0.002250316,0.1993761,0.0004809081,0.0004627685,0.0005445058,0.003292863,0.0003711352,0.01902894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01549807,"threshold_uncertainty_score":0.03081572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03869101775410788,"score_gpt":0.2617709655562732,"score_spread":0.2230799478021653,"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."}}