{"id":"W7135090694","doi":"10.1109/camsap66162.2025.11423976","title":"Bayesian Koopman Time Series Forecasting","year":2025,"lang":"","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Operator (biology); Bayesian probability; Bayesian inference; Nonlinear system; Sampling (signal processing); Set (abstract data type); Series (stratigraphy); Dynamical systems theory; Linear map","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000419878,0.0005293907,0.0005331413,0.0003598829,0.0007597643,0.001745407,0.00208993,0.0002472684,0.001819211],"category_scores_gemma":[0.0001503849,0.0004931659,0.0001914325,0.002185565,0.0002554822,0.0019057,0.001189886,0.0003912358,0.000695457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000860097,"about_ca_system_score_gemma":0.0009023084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004518085,"about_ca_topic_score_gemma":0.00003294238,"domain_scores_codex":[0.9965134,0.00009447058,0.0008133586,0.001156982,0.000388413,0.001033342],"domain_scores_gemma":[0.9979901,0.0001246889,0.000224372,0.001111627,0.0002984199,0.0002508299],"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":[0.00003047611,0.0001568413,0.001273721,0.0005300274,0.0001112365,0.00009855539,0.0007281342,0.00003943604,0.0002138496,0.5947989,0.01551568,0.3865031],"study_design_scores_gemma":[0.0006756328,0.000386032,0.001318569,0.001184197,0.0000802759,0.0001358228,0.0001278005,0.8362659,0.005602184,0.1307054,0.02236032,0.00115795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001598411,0.0003769224,0.6901551,0.007945403,0.0007737378,0.0002293707,0.00000388974,0.0002356982,0.30012],"genre_scores_gemma":[0.7105111,0.00006897723,0.1000737,0.001777812,0.0001716632,0.00002260915,0.000003295921,0.00002355656,0.1873473],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8362264,"threshold_uncertainty_score":0.999752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258523937330747,"score_gpt":0.2303950061959236,"score_spread":0.2178097668226161,"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."}}