{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001005515,0.00005461222,0.0001008705,0.0004792422,0.00008564009,0.0000217013,0.0002188192,0.00005491157,0.0007178195],"category_scores_gemma":[0.001374614,0.00004458313,0.00006011902,0.002978087,0.00009737002,0.0001198155,0.000110547,0.00006555452,0.00228657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000146773,"about_ca_system_score_gemma":0.00001199675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002389701,"about_ca_topic_score_gemma":2.186977e-7,"domain_scores_codex":[0.9986932,0.00002407421,0.0003045512,0.0001954021,0.0006829967,0.00009976486],"domain_scores_gemma":[0.9989941,0.0004661192,0.0001202057,0.0003383643,0.00004374822,0.00003749696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000422495,0.0003142505,0.06102669,0.00001079842,0.00002980193,0.000005033716,0.0002148499,0.008746368,0.03117836,0.008446149,0.742892,0.1470934],"study_design_scores_gemma":[0.0001511706,0.0001818251,0.2583388,0.000005198642,0.00001151241,0.000009229143,0.00008233797,0.05419326,0.007906123,0.03000293,0.6489898,0.0001277946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444075,0.00008754781,0.1335539,0.001945431,0.000475215,0.0004224702,0.0008682606,0.0007960914,0.01744363],"genre_scores_gemma":[0.8669314,0.0001756588,0.05742425,0.00008962183,0.00041472,0.00005863879,0.0002777867,0.00003933784,0.07458864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1973121,"threshold_uncertainty_score":0.9984903,"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."}}