Data mining based wireless network traffic forecasting

In this paper, we propose an approach for predicting time series. This approach is based on the Stationary Wavelet Transform (SWT) and two types of forecasting models, such as based on Auto-Regressive Integrated Moving Average (ARIMA) and based on Artificial Neural Networks (ANNs). The forecasting performance of these models was evaluated using three well-known evaluation criteria: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). Results show that ANN performs better than ARIMA based forecasting technique for small future time intervals. However, ARIMA models can capture the behavior of the time series and is suitable for long term prediction. We present two applications for wireless networks traffic forecasting, the prediction of the moment when a specified Base Station (BS) will saturate (long term prediction) and the prediction of traffic anomalies (short term prediction).