Wavelet Methods For Time Series Analysis Percival Pdf 25

This paper presents some time series analysis techniques for time-series data (TST, in time-series terminology). In particular, the paper develops a nonparametric test for the presence of a unit root in a time series, based on the different behavior of the spectra of a unit root process and a short memory stationary process. The approach is based on the continuous wavelet transform (CWT) and on the assumption that a time series is a realization of a discrete stochastic process. The paper shows the consistency of the approach and provides some theoretical results and an empirical study. The paper concludes with a brief discussion about the relevance of the time series methods in neuroscience applications, such as in the analysis of EEG and MEG time series data.

This paper describes a new approach to the analysis of biological time-series data. The approach is based on the well-known Fourier transform and uses wavelets as a tool to decompose the transform into its underlying frequency components. The idea is to use the wavelet transform to decompose the data into a series of bandpass filtered data. This new approach to the analysis of biological time-series data is described in detail and applied to the analysis of data recorded in human subjects performing the Stroop task. The results show that the new approach can detect an effect of task instructions on the Stroop effect.

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