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Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival
Publisher: Cambridge University Press

Details of scaling and translation of the Morlet wavelet with an interactive Demonstration. Wavelet analysis techniques, while not as commonly understood as Fourier analysis, are nonetheless frequently applied to problems in which time and frequency information are desired simultaneously. CSSPM - Percival D.B., Walden A.T. Algorithm Group (NAG) in areas such as optimization, curve and surface fitting, FFTs, interpolation, linear algebra, wavelet transforms, quadrature, correlation and regression analysis, random number generators and time series analysis. From an aware point of view, the usage of periodogram methods discussed within my previous post on Modern Time Analysis of Black Swans seems to be reasonable only in case of searching for deterministic and stationary modulations. Computational Intelligence In Time Series Forecasting Popovic 2005.pdf. In this way, any sudden event in a time series can be determined to reasonable accuracy through the wavelet method, regardless of any particular frequency that may be associated with the phenomenon. Also, lossy method of image compression on the Mandelbrot set. An introduction to the theory of time-frequency analysis and wavelet analysis for the financial time-series. - Wavelet methods for time series analysis - CUP 2000 - ISBN 0521640687.djvu. When applied to time-series data, wavelet analysis involves a transform from the given one-dimensional time series to a two-dimensional time-frequency image.