Building a Financial Time Series Analysis System with Wavelet Transformations
Introduction to Financial Time Series Analysis Financial time series analysis is a crucial aspect of modern finance, enabling us to predict market trends, manage risks, and make informed investment decisions. These series are complex and often non-stationary, making traditional analysis methods less effective. This is where wavelet transformations come into play, offering a powerful tool for decomposing and analyzing these series. What are Wavelet Transformations? Wavelet transformations are mathematical tools that allow us to analyze signals in both time and frequency domains simultaneously. Unlike Fourier transformations, which are global and do not provide localized information, wavelet transformations use localized basis functions. This makes them particularly useful for analyzing non-stationary signals, which are common in financial data. ...