{"id":"W4388678018","doi":"10.1007/978-3-031-48232-8_43","title":"Machine Learning for Time Series Forecasting Using State Space Models","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Autoregressive model; Time series; Series (stratigraphy); State space; Artificial intelligence; Model selection; Machine learning; Flexibility (engineering); State-space representation; Selection (genetic algorithm); Strengths and weaknesses; Data mining; Algorithm; Econometrics; Mathematics; Statistics","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":[0.0005017335,0.000793478,0.0009615642,0.0004376683,0.0002001781,0.0008788765,0.0007854425,0.0008579682,0.005614313],"category_scores_gemma":[0.001510435,0.0004234613,0.0008490171,0.001036008,0.0003132462,0.001179459,0.0004740375,0.001916339,0.002782053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004138882,"about_ca_system_score_gemma":0.0002614781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272544,"about_ca_topic_score_gemma":0.002041157,"domain_scores_codex":[0.9997957,0.00006003532,0.00001806084,0.00004732032,0.00006632009,0.00001254429],"domain_scores_gemma":[0.9995226,0.0003492945,0.00002168643,0.00004937765,0.00005025392,0.00000683329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000752923,0.00007905019,0.0003581379,0.0004268314,0.0001393013,0.00008908447,0.00007679884,0.4586876,0.004175337,0.0625938,0.01379076,0.459508],"study_design_scores_gemma":[0.000003607446,0.00001330111,0.0001238432,0.00002222887,0.000012066,0.00001941551,0.000003495143,0.9713433,0.0006624341,0.02275852,0.005030497,0.000007319489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002712327,0.006403095,0.9841141,0.000273138,0.0002880818,0.00001649174,0.0001141916,0.000826302,0.005252363],"genre_scores_gemma":[0.2888408,0.01517089,0.6398958,0.0002466094,0.0009988931,0.000238429,0.001539856,0.0005754362,0.05249338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005614313,"threshold_uncertainty_score":0.01878172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04094344013326841,"score_gpt":0.2400563468381648,"score_spread":0.1991129067048965,"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."}}