Multimedia Traffic Prediction Based on Wavelet-and Spline-extrapolation

The task of predicting self-similar traffic of IoT network objects with a significant number of pulsations and the property of long-term dependence is considered, which makes it difficult to predict in practice. Using the methods of wavelet- and spline-extrapolation based on Haar-wavelet, quadratic and B-spline function, the results of prediction of multimedia traffic are obtained. We compare the results of traffic prediction based on the Haar-wavelet and the quadratic and B-spline function using wavelet- and spline-extrapolation. This will allow the choice of one or another extrapolation method to improve the accuracy of the prediction, while ensuring scalability and the ability to use it for various IoT applications to prevent network overloads.