{"id":"W4313635619","doi":"10.37119/jpss2022.v20i1.511","title":"Forecasting of Immigrants in Canada using Forecasting models","year":2022,"lang":"en","type":"article","venue":"Journal of Probability and Statistical Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Autoregressive integrated moving average; Mean absolute percentage error; Akaike information criterion; Mean squared error; Statistics; Bayesian information criterion; Econometrics; Autoregressive model; Moving average; Mean absolute error; Bayesian probability; Mathematics; Autoregressive–moving-average model; Box–Jenkins; Time series","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008143736,0.0008014379,0.0004588351,0.001216616,0.001153819,0.001167004,0.0009542012,0.0004515378,0.001164965],"category_scores_gemma":[0.002016761,0.0001799664,0.000735584,0.002165012,0.0002704451,0.0004086728,0.0005027949,0.0006819493,0.0002764651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00703218,"about_ca_system_score_gemma":0.0112439,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9633359,"about_ca_topic_score_gemma":0.9466431,"domain_scores_codex":[0.9997039,0.00003549167,0.00001473427,0.00006138356,0.00009669789,0.00008772343],"domain_scores_gemma":[0.9993813,0.0001125977,0.00005940401,0.00002103031,0.0003689596,0.00005658978],"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":[0.0002619057,0.0001223303,0.1325191,0.0001717913,0.0001138054,0.0004427278,0.0004621917,0.7604142,0.001183955,0.003311996,0.01175684,0.08923924],"study_design_scores_gemma":[0.00001031419,0.00001450379,0.01871556,0.00002014604,0.00002562275,0.00001389766,0.0003148055,0.9785406,0.0003404479,0.0003974129,0.001583617,0.00002308898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646817,0.0009056638,0.01921665,0.001088317,0.0001178788,0.00009640019,0.005514878,0.0005729742,0.007805486],"genre_scores_gemma":[0.9807507,0.0007143578,0.01043565,0.00005651825,0.00002453738,0.00003469588,0.004244179,0.00002498528,0.00371441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03666407,"threshold_uncertainty_score":0.07375991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05201014812807485,"score_gpt":0.2303020700837979,"score_spread":0.1782919219557231,"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."}}