{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002867064,0.0001234328,0.0001738646,0.00003603084,0.0001075995,0.0001542541,0.0002981327,0.00004314816,0.00001560875],"category_scores_gemma":[0.00008878236,0.0001064498,0.00007368418,0.0002452777,0.00001861186,0.0005004796,0.0002169594,0.00006645889,0.0000117507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001639479,"about_ca_system_score_gemma":0.0001350989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004374962,"about_ca_topic_score_gemma":0.00001655722,"domain_scores_codex":[0.9990113,0.00005075074,0.0001576654,0.0003627496,0.0001212226,0.0002963005],"domain_scores_gemma":[0.9992052,0.0001007026,0.00004640501,0.0003830124,0.0001715589,0.0000931402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002487454,0.00008113088,0.00002662428,0.00008304751,0.00006544289,0.00005099851,0.002672566,0.003197107,0.01627851,0.6771786,0.03397251,0.2663686],"study_design_scores_gemma":[0.0001493525,0.00001822342,0.00000276912,0.000007672585,0.000003637751,0.00001796073,0.000002851046,0.7877944,0.01129383,0.1996657,0.0009190096,0.0001246094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003926751,0.00005930753,0.9905077,0.002740876,0.00009401166,0.0001265908,0.000007284768,0.0001623819,0.005909225],"genre_scores_gemma":[0.003926248,0.000006144274,0.9491591,0.0009649726,0.00002663469,0.00001730464,0.000004365132,0.00001220266,0.04588299],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7845972,"threshold_uncertainty_score":0.4340897,"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."}}