{"id":"W4402438656","doi":"10.11159/icmie24.110","title":"Demand Forecasting Model To Reduce The Mean Absolute Percentage Error By Applying Seasonal Breakdown Tools In A Sme In The Tourism Sector","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Tourism; Mean absolute error; Mean absolute percentage error; Demand forecasting; Econometrics; Mean squared error; Environmental science; Statistics; Computer science; Operations research; Economics; Engineering; Mathematics; 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":[0.0006680529,0.0003393568,0.0005143782,0.0003815932,0.0002585721,0.0004654118,0.0004582981,0.0004716918,0.001993437],"category_scores_gemma":[0.001398162,0.0002008145,0.0003885858,0.0005397887,0.00008420287,0.0003980533,0.0002179079,0.0006433623,0.0002775003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004038265,"about_ca_system_score_gemma":0.0006755251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0229769,"about_ca_topic_score_gemma":0.01476036,"domain_scores_codex":[0.9998817,0.00003640244,0.00001034718,0.0000287396,0.00001995461,0.00002278467],"domain_scores_gemma":[0.9994506,0.0003233415,0.00003440204,0.00002734488,0.0001375038,0.00002680606],"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":[0.0001716249,0.0001700428,0.004610517,0.00004003528,0.00004380918,0.0000731172,0.00005281474,0.9328444,0.002857201,0.00135307,0.002173963,0.05560936],"study_design_scores_gemma":[0.000001151819,0.000006455779,0.0002676243,6.982929e-7,0.000002592511,0.000002286553,0.000004753692,0.9994386,0.0001318371,0.00009505418,0.00004766742,0.000001380853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.528521,0.0002493319,0.46368,0.0005794934,0.000139643,0.00003871557,0.0006165199,0.001461304,0.004713986],"genre_scores_gemma":[0.9726039,0.00005802101,0.02515991,0.00003412048,0.00001840711,0.00003011931,0.0002983792,0.00004915893,0.001748091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0229769,"threshold_uncertainty_score":0.0456863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141292032048554,"score_gpt":0.2283015223476833,"score_spread":0.2068886020271978,"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."}}