Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?
by Mohammad Hossein Vaghefzadeh; Behrooz Karimi; Abbas Ahmadi
International Journal of Industrial and Systems Engineering (IJISE), Vol. 43, No. 4, 2023

Abstract: The present research applies two different modelling approaches to evaluate the historical demand for a special mobile value-added service (VAS) that is offered and delivered to airline customers. The first method is deterministic and includes non-stationary time series models that cover both mean and variance fluctuation, as well as seasonality effect, in the dataset. The second method is a metaheuristic approach in the form of artificial neural network time series analysis (ANN-TSA). These methods are used to evaluate the power of each category and to choose the best model based on appropriate criteria. The results show that non-stationary time series models outperform ANN-TSA, as indicated by the smaller number of errors in the simulation of the demand dataset.

Online publication date: Fri, 24-Mar-2023

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