{"id":"W4386715973","doi":"10.18280/isi.280404","title":"A Novel Hybrid Approach for Daily Tourism Arrival Forecasting: The PROPHET-Bayesian Gaussian Process-Forward Neural Network Model","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Artificial neural network; Gaussian process; Bayesian probability; Computer science; Tourism; Process (computing); Econometrics; Artificial intelligence; Gaussian; Machine learning; Geography; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004489592,0.0002393841,0.0001939318,0.0001945478,0.0003700732,0.0002499997,0.0003100999,0.00008658083,0.000001552793],"category_scores_gemma":[0.00006025403,0.0001933229,0.0001035615,0.0005000696,0.0000693019,0.001387536,0.00005548359,0.0001822684,0.000007029307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117911,"about_ca_system_score_gemma":0.00003130411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004325069,"about_ca_topic_score_gemma":0.000001959503,"domain_scores_codex":[0.9985851,0.00001153547,0.0005120495,0.000140566,0.0002547249,0.0004959966],"domain_scores_gemma":[0.9994239,0.00002809222,0.0001320867,0.0002476856,0.00009016675,0.00007803012],"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":[0.00002245182,0.000008607518,0.00001588639,0.0006268205,0.00004319047,7.513167e-7,0.001579716,0.9247563,0.00001928844,0.001403504,0.02681787,0.0447056],"study_design_scores_gemma":[0.0003473206,0.00004381483,0.00008927101,0.00005620917,0.00002829139,0.00002814192,0.0005313842,0.9948285,0.0001144404,0.001791364,0.001914885,0.0002263523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00743244,0.00002235945,0.9731638,0.0000705204,0.0003006964,0.001362096,0.00005077657,0.005954305,0.01164296],"genre_scores_gemma":[0.9810624,0.0000142827,0.01706186,0.0001277507,0.0002226867,0.001043949,0.0003572738,0.00004154379,0.00006829286],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9736299,"threshold_uncertainty_score":0.7883478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204044778451424,"score_gpt":0.2213692444303862,"score_spread":0.199328796645872,"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."}}