{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002415495,0.0004529655,0.0005762658,0.0006094846,0.0009252002,0.0007386652,0.0004802613,0.0002746435,0.00007929953],"category_scores_gemma":[0.002018575,0.0003615467,0.0001156272,0.00106531,0.0003657454,0.002066083,0.0004848856,0.0004836482,0.00001986046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863979,"about_ca_system_score_gemma":0.0002312372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001897002,"about_ca_topic_score_gemma":0.00003725945,"domain_scores_codex":[0.9960083,0.0003882114,0.001040496,0.001022993,0.0009252626,0.0006147344],"domain_scores_gemma":[0.9971817,0.0009092155,0.0006756004,0.0004287098,0.0006052467,0.0001995029],"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":[0.0003073269,0.00005467393,0.01196298,0.00005264344,0.0001467131,0.0001127596,0.004951016,0.8184894,0.01659843,0.004697402,0.0007653975,0.1418613],"study_design_scores_gemma":[0.0005010944,0.00008722937,0.0009296242,0.0001648809,0.00002954355,0.00008480615,0.001616101,0.9654249,0.0007951863,0.02992874,0.00008243822,0.0003554409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810249,0.0001009794,0.1164549,0.0005763212,0.0001539637,0.0004873455,0.0001699881,0.0006886983,0.0003429339],"genre_scores_gemma":[0.9546167,0.00007372,0.04448736,0.0001177751,0.00007161978,0.0000795764,0.0001462219,0.00004821151,0.0003587859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1469355,"threshold_uncertainty_score":0.9998837,"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."}}