{"id":"W4386898902","doi":"10.18280/i2m.220404","title":"Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models","year":2023,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Autoregressive integrated moving average; STAR model; Series (stratigraphy); SETAR; Time series; Nonlinear autoregressive exogenous model; Autoregressive–moving-average model; Moving average; Moving-average model; Econometrics; Computer science; Statistics; Mathematics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002867577,0.0007138647,0.0007257706,0.001550583,0.0003328114,0.0016802,0.001026443,0.000755546,0.001289755],"category_scores_gemma":[0.007744763,0.0002656081,0.001160676,0.00287601,0.0002775336,0.00144188,0.000693063,0.001314661,0.0005805837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005150806,"about_ca_system_score_gemma":0.001270904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00786183,"about_ca_topic_score_gemma":0.008483996,"domain_scores_codex":[0.9989132,0.0004359038,0.0001027417,0.0001873925,0.0002984536,0.00006239638],"domain_scores_gemma":[0.9984002,0.000966084,0.0002522297,0.0001066902,0.0002490086,0.00002587025],"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":[0.0001081428,0.0001473327,0.0126586,0.0004659917,0.0003526714,0.0002634649,0.0003568833,0.6201964,0.003847634,0.06380899,0.005090498,0.2927034],"study_design_scores_gemma":[0.000004527742,0.00004517216,0.002355658,0.00005286018,0.00003823623,0.00004353243,0.00008163273,0.9804584,0.0007551608,0.0118578,0.00428318,0.00002378132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03273841,0.001707907,0.9597865,0.0006612939,0.0001426601,0.00008713605,0.0005418169,0.0006501598,0.00368411],"genre_scores_gemma":[0.5185277,0.004355959,0.4700046,0.0001692935,0.00021591,0.0003037736,0.001597861,0.0001191354,0.0047058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00786183,"threshold_uncertainty_score":0.01563215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.13866932070391,"score_gpt":0.3607429630022279,"score_spread":0.2220736422983179,"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."}}