Abstract
This work deals with statistical modeling and forecasting of telecommunications data. Main mobile traffic events (SMS, Voice calls, Mobile data) are smoothed using B-spline functions and later analyzed in a functional framework. Functional linear auto-regression models are fitted using both bottom-up and topdown design methodologies. The advantages and disadvantages of both approaches for the prediction of mobile telephone users’ habits are discussed.
Highlights
Modelling and predicting telecommunication parameters is a common challenge for over 20 years, see Frost and Melamed [12] and Meier-Hellstern et al [14]
Telecommunications data are treated as observations of random curves
An overview of Functional Data Analysis (FDA) is provided by Ramsay and Silverman [17], Ferraty and Vieu [11] and more recently by Wang et al [19]
Summary
Modelling and predicting telecommunication parameters is a common challenge for over 20 years, see Frost and Melamed [12] and Meier-Hellstern et al [14]. MVNOs purchase an amount of main mobile network products (SMS, Voice calls, Mobile data) at wholesale prices from MNO and sell them to the customers at their prices Such business model is popular around the world (e.g., Brazile Telecom, Uno Mobile Italy, Carrefour Taiwan Mobile, Samatel Oman, Equitel Kenya, etc.). Usage of three mobile products, Voice calls, SMS and Mobile data are investigated following functional data analysis methodology. N , where = 1, 2, 3 corresponds to the three metrics (SMS, Voice, Mobile data), j corresponds to a consumer and k indicates month as time series unit. These data are interpreted as measurements of monthly curves x(jk) = (x(jk)(t), t ∈ [0, 1]), x(jk)i.
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