Comparative Analysis of the Methods of Wavelet-and Spline-extrapolation in Problems of Predicting Self-similar Traffic

Abstract

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 method of wavelet extrapolation based on Haar-wavelet, the results of prediction of self-similar traffic are obtained. We compared the results of traffic prediction based on the Haar-wavelet and the cubic spline function using wavelet and spline extrapolation. This will allow you to choose 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.