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

What you probably want to know is something like the average error is 1 °C or the 95% confidence interval is ±2 °C. It should be remarked that the definition of functional connections in previous FW analysis methods [4], [6]–[11] is basically based on the Pearson's correlation approach (two signals are correlated if we can predict the variations of one as a function of the other). Starting with the raw data, temporal trends and spatial noise were removed by linearly detrending the time series for each grid cell and then applying a three by three Gaussian filter. Filtering and wavelets and Fourier. Dangles1,2,3 time series were acquired over the same period. Insightful has released the following time series packages via CSAN at http://csan.insightful.com: FRACTAL: stochastic fractal time series and nonlinear modeling. If the value of In this paper, we develop a method to construct a new type of FW from regional fMRI time series, in which PS degree [24], [25] between two regional fMRI time series is taken as the functional connection strength. Furthermore, we found that our method permits to detect glacial signal in supposedly non-glacial sites, thereby evidencing glacial meltwater infiltrations. - Wavelet Methods for Time Series Analysis, by Percival and Walden: standard theoretical text on wavelets. Econometric Analysis, by Greene: classic text on theoretical econometrics. Technical Note: Using wavelet analyses on water depth time series to detect glacial influence in high-mountain hydrosystems. WMTSA: wavelet methods for time series analysis.