Comparing Time Series Prediction Approaches for Telecom Analysis

Abstract

Prediction of consumption has several applications in industry, including to support strategic decisions, market offering, and value proposition. In telecommunications industry, it can also be used in network resources management and in guaranteeing quality of service to users. But in order to make good predictions, one should choose the algorithm that is best fitted to the considered time series and also configures the parameters correctly. In this chapter, we discuss the use of time series forecasting algorithms over telecommunications data. We evaluate the use of Auto-Regressive Integrated Moving Average (ARIMA), Prophet (launched by Facebook in 2017), and two neural network algorithms: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). We ran those algorithms over real data about Internet data consumption and mobile phone cards recharges, in order to forecast time periods of distinct sizes. Forecasted values were qualified in terms of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Obtained results show that ARIMA is the algorithm that is best suited to most cases.