{"id":"W3048808678","doi":"10.4018/978-1-7998-3805-0.ch009","title":"Forecasting Sales and Return Products for Retail Corporations and Bridging Among Them","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in logistics, operations, and management science book series","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sales forecasting; Autoregressive integrated moving average; Product (mathematics); Lost sales; Bridging (networking); Sales management; Bridge (graph theory); Marketing; Business; Retail sales; Demand forecasting; Operations research; Computer science; Time series; Engineering; Mathematics; Machine learning","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":[0.0006966568,0.0005980349,0.0003443,0.001151135,0.0002309504,0.001089157,0.0004606211,0.0006953057,0.00237893],"category_scores_gemma":[0.002442713,0.0002497197,0.0003227588,0.001647175,0.0001034802,0.00237198,0.0002985286,0.0006237773,0.0009537576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418644,"about_ca_system_score_gemma":0.000349198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006370931,"about_ca_topic_score_gemma":0.006228732,"domain_scores_codex":[0.9998197,0.00003398575,0.00001190444,0.00005287933,0.00006279181,0.00001876294],"domain_scores_gemma":[0.9992924,0.0004197969,0.00007923347,0.00004871489,0.0001274169,0.00003239323],"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":[0.0004251488,0.0004019018,0.1321487,0.000168352,0.00007292873,0.0004662992,0.0003671866,0.2989345,0.00449012,0.008548689,0.01509515,0.538881],"study_design_scores_gemma":[0.0000061738,0.00007323655,0.01533704,0.00003323537,0.000024398,0.00005245163,0.0003392376,0.9735516,0.001626649,0.005111351,0.003827937,0.00001679308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8342798,0.003653426,0.1364368,0.002498296,0.0002011764,0.0001159104,0.002025448,0.00152161,0.0192674],"genre_scores_gemma":[0.9357283,0.001809975,0.05421198,0.00008340749,0.00006458547,0.00003634063,0.001931929,0.00007619236,0.006057282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006370931,"threshold_uncertainty_score":0.01266772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1492831530337002,"score_gpt":0.3332176823454097,"score_spread":0.1839345293117095,"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."}}