Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?

Mohammad Hossein Vaghefzadeh, Behrooz Karimi and Abbas Ahmadi

International Journal of Industrial and Systems Engineering, 2023, vol. 43, issue 4, 555-581

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.

Keywords: time series; analysis; non-stationary; artificial neural network; mobile value-added; seasonal effect; demand; forecasting. (search for similar items in EconPapers)
Date: 2023
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