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

Author

Listed:
  • Mohammad Hossein Vaghefzadeh
  • Behrooz Karimi
  • Abbas Ahmadi

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.

Suggested Citation

  • Mohammad Hossein Vaghefzadeh & Behrooz Karimi & Abbas Ahmadi, 2023. "Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 43(4), pages 555-581.
    • Handle: RePEc:ids:ijisen:v:43:y:2023:i:4:p:555-581
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