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In finding a continuous function of time from time series data, one must eliminate noise that arise in measurements. Denoising is particularly important when one wants to compute the time derivative of the function, which is very sensitive to noise. The compactly supported B-spline wavelet provides us with an ideal method of finding a continuous function from such data. We have developed a Mathematica package SplineWavelet.m for wavelet analysis, with which one can estimate a smooth function from noisy time series data. Some details of the implementation are discussed, and an example time series data are analyzed using the package, focusing our attention to the time derivative of the function.
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