{"id":"W4392200264","doi":"10.18280/isi.290129","title":"Demand Prediction for Food and Beverage SMEs Using SARIMAX and Weather Data","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direktorat Jenderal Pendidikan Tinggi; Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","keywords":"Weather prediction; Demand forecasting; Business; Computer science; Meteorology; Marketing; Geography","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":[0.0005489345,0.0006362992,0.0003497074,0.0006239003,0.0001819779,0.0007066399,0.0004269335,0.0004418342,0.00170662],"category_scores_gemma":[0.00185131,0.0002453236,0.0006404818,0.0008813306,0.0001322557,0.0005913267,0.0004248749,0.0004923011,0.000502286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004865076,"about_ca_system_score_gemma":0.000422959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01750298,"about_ca_topic_score_gemma":0.01499509,"domain_scores_codex":[0.9997603,0.0000796516,0.00002621466,0.00006336621,0.0000365,0.00003394665],"domain_scores_gemma":[0.9992661,0.0004132826,0.00009311287,0.0000512639,0.0001335514,0.00004271507],"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":[0.0009783721,0.0006324378,0.4355929,0.0003412154,0.0002281446,0.0009047842,0.0003426414,0.4809489,0.005630777,0.001687026,0.00461542,0.06809735],"study_design_scores_gemma":[0.00001270902,0.0001634389,0.06836054,0.00001669193,0.00002533789,0.00006549929,0.0003329842,0.9282617,0.001292312,0.0004897889,0.0009557144,0.00002332807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873667,0.0001332068,0.007260353,0.0002556265,0.00002446669,0.00002640857,0.002086526,0.0001342339,0.002712455],"genre_scores_gemma":[0.9948022,0.00006692843,0.002538732,0.00001540786,0.000006825535,0.00001412786,0.001914815,0.000007367217,0.0006334688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01750298,"threshold_uncertainty_score":0.03480226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681921674110779,"score_gpt":0.2426103867945947,"score_spread":0.2057911700534869,"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."}}