{"id":"W4319262068","doi":"10.1002/env.2792","title":"Nonlinear prediction of functional time series","year":2023,"lang":"en","type":"article","venue":"Environmetrics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Functional principal component analysis; NOP; Nonlinear system; Computer science; Series (stratigraphy); Functional data analysis; Time series; Multivariate statistics; Preprocessor; Principal component analysis; Covariance; Linear model; Algorithm; Data mining; Artificial intelligence; Mathematics; Machine learning; Statistics","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":[0.001400991,0.0006668316,0.0005058335,0.000595046,0.0002538428,0.000570531,0.0008385929,0.0006526292,0.001342817],"category_scores_gemma":[0.005626158,0.0002197515,0.0005249162,0.0005976805,0.0005674893,0.0009208784,0.0006092872,0.001131115,0.0002818075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005744582,"about_ca_system_score_gemma":0.0006185312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007915578,"about_ca_topic_score_gemma":0.004064297,"domain_scores_codex":[0.9994722,0.0001825083,0.00002481553,0.0001311285,0.0001509012,0.00003842109],"domain_scores_gemma":[0.9981944,0.001035902,0.0001957867,0.0001417189,0.0003876403,0.00004458329],"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":[0.00007696687,0.000057772,0.004540724,0.00008762215,0.00004955966,0.0001083596,0.00005650641,0.9049745,0.002845226,0.009611075,0.001185326,0.07640637],"study_design_scores_gemma":[9.024562e-7,0.00000438639,0.0003051591,0.000001722885,0.000001340524,0.000004591332,0.00000200949,0.9983749,0.000200849,0.001005369,0.00009647472,0.000002236124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03731133,0.0001993586,0.9605131,0.0002506777,0.00007710743,0.00002799384,0.0001074759,0.0002201095,0.001292842],"genre_scores_gemma":[0.8731332,0.0003538564,0.122469,0.00009437812,0.0001217312,0.0001042781,0.0003177739,0.00007092424,0.003334788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007915578,"threshold_uncertainty_score":0.01573902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1290945052483437,"score_gpt":0.3212951263771008,"score_spread":0.192200621128757,"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."}}