Evaluating Wavelet Analysis in the Estimation of Network Congestion with Time Series Analysis

Wavelet analysis is an effective manner to estimate community congestion based on time collection analysis. Wavelet evaluation is an effective sign-processing method that can locate discontinuities in time collection information without linear filters. Conventional time series evaluation requires the statistics to be stationary, a situation that is frequently violated in networks with non-linear response instances. Via decomposing the signal and setting apart the discontinuities, wavelet analysis can assist as it should be estimated network congestion. In this paper, we exhibit wavelet evaluation's effectiveness inside the community congestion estimation with time series analysis. We compare the results of wavelet analysis with traditional techniques for assessing the community's overall performance and discuss the advantages of using this method. The consequences of our experiments display wavelet analysis's superiority in accuracy in predicting community congestions. We conclude with a discussion of capacity programs of wavelets inside the location of community control.