{"id":"W2997916132","doi":"10.1504/ijsom.2020.10026105","title":"Carsharing customer demand forecasting using causal, time series and neural network methods: a case study","year":2020,"lang":"en","type":"article","venue":"International Journal of Services and Operations Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Exponential smoothing; Demand forecasting; Computer science; Autoregressive integrated moving average; Time series; Artificial neural network; Operations research; Service quality; Customer satisfaction; Service (business); Business; Marketing; Artificial intelligence; 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":[0.001509518,0.0006395644,0.0004530308,0.001044077,0.0006358281,0.0008342381,0.0008997683,0.001637119,0.001606604],"category_scores_gemma":[0.003068764,0.0003332784,0.0006657179,0.001593129,0.0004243752,0.0009154698,0.000481683,0.0008802385,0.0001582576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468941,"about_ca_system_score_gemma":0.0006565452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04089427,"about_ca_topic_score_gemma":0.03413957,"domain_scores_codex":[0.9994645,0.0002235725,0.00003881218,0.00006327779,0.000130586,0.00007922989],"domain_scores_gemma":[0.9967204,0.002473474,0.0001572177,0.0001441576,0.0004118482,0.00009292031],"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":[0.0005833594,0.001392664,0.04200913,0.0002936808,0.000129764,0.002776445,0.0004221192,0.8912948,0.00236235,0.004007476,0.002149265,0.05257902],"study_design_scores_gemma":[0.00001946283,0.0001414978,0.005418995,0.000006084674,0.00001757606,0.00007750143,0.0002642401,0.9920493,0.001096316,0.0004472767,0.0004439515,0.00001776281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801276,0.0002766608,0.01601735,0.0004243875,0.00002654947,0.00008513123,0.0003770741,0.0001018139,0.002563487],"genre_scores_gemma":[0.9904742,0.000213015,0.008105857,0.00001668542,0.00001229962,0.00003682513,0.0001988319,0.000008604775,0.0009337554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04089427,"threshold_uncertainty_score":0.08131248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0364783619102067,"score_gpt":0.3052839601729266,"score_spread":0.2688055982627199,"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."}}