{"id":"W3005055051","doi":"10.1504/ijbda.2022.126806","title":"A comparative study of univariate time-series methods for sales forecasting","year":2022,"lang":"en","type":"article","venue":"International Journal of Business and Data Analytics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive integrated moving average; Univariate; Mean squared error; Time series; Series (stratigraphy); Moving average; Sales forecasting; Box–Jenkins; Multiplicative function; Computer science; Econometrics; Metric (unit); Statistics; Mean absolute percentage error; Autoregressive–moving-average model; Artificial neural network; Autoregressive model; Mathematics; Multivariate statistics; Artificial intelligence; Economics; Operations management","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00745862,0.0009267299,0.0008434705,0.003233227,0.0003012861,0.001305679,0.0008771147,0.0007335155,0.002135498],"category_scores_gemma":[0.01720729,0.000243441,0.0009987464,0.004138735,0.0002352583,0.002255289,0.0004394157,0.001148327,0.0006946642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005930112,"about_ca_system_score_gemma":0.0006172421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007120822,"about_ca_topic_score_gemma":0.007512803,"domain_scores_codex":[0.9975285,0.0009840304,0.0002073448,0.0003658129,0.0008397084,0.0000746669],"domain_scores_gemma":[0.9810026,0.01540791,0.0007326817,0.0009484903,0.001713489,0.0001949282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008128677,0.0003110623,0.03532393,0.001062253,0.001135059,0.0001060841,0.0003475218,0.1430065,0.002244554,0.008607241,0.00572543,0.8013175],"study_design_scores_gemma":[0.00004496997,0.0004248437,0.02614926,0.0002415201,0.0002183971,0.000145538,0.0003135524,0.9580784,0.002789366,0.003864273,0.007653907,0.00007598389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.449588,0.05187269,0.4646451,0.002663488,0.001459205,0.000214941,0.002492934,0.003219261,0.02384435],"genre_scores_gemma":[0.8186772,0.01467819,0.1603267,0.0001606966,0.0005812267,0.0001068891,0.001650528,0.0003212297,0.003497317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00745862,"threshold_uncertainty_score":0.0394454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4310993992184448,"score_gpt":0.517864563100021,"score_spread":0.08676516388157629,"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."}}