Abstract
Latterly, wavelet is used in various application of statistics. Wavelet is a method without parameter which used in signal analysis, data compression, and time series analysis. Wavelet thresholding is a method which reconstructing the largest number of wavelet coefficients. Only the coefficients are greater than a specified value which taken and the rest coefficients are ignored, because considered null. Certain value is called the threshold value. The level of smoothness estimation are determined by some factor such as wavelet functions, the type of thresholding functions, level of resolutions and threshold parameters. But most dominant factor is threshold parameter. Because that was required to select the optimal threshold value. At the simulation study was analyzing of the stasioner, nonstasioner and nonlinier data. Wavelet thresholding method gives the value of Mean Square Error (MSE) which is smaller than the ARIMA. Wavelet thresholding is considered quite so well in the analysis of time series data.
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