Wavelet analysis

Time series information is not always sufficient to get insight into the data. Sometimes the frequency content of the data also contains important information about the data. In the time domain, Fourier transformation (FT) captures the frequency-amplitude of the data but it does not show when in time this frequency has happened. In the case of stationary data, all frequency components exist at any point in time but this is not true for non-stationary data. So, FT does not fit for non-stationary data. Wavelet transformation (WT) has the capacity to provide time and frequency information simultaneously in the form of time-frequency. WT is important to analyze financial time series as most of the financial time series are non-stationary. In the remainder of this chapter, wavelet analysis (WT), I will help you understand how to solve non-stationary data in R using wavelets analysis. Stock price/index data requires certain techniques or transformations to obtain further information...