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

Venue: Statistics Building (c/o Victoria- and Bosman streets, Stellenbosch), Room 2021. The statistics group's research projects include the modelling of random phenomena, methods for the analysis of data, and computational techniques for performing this modelling and analysis. The applications of this research are The PhD students are being recruited in the main research areas of the Department; mathematical analysis, mathematics of inverse problems, stochastics, spatial and computational statistics, time-series analysis. Focus on wavelet analysis in finance and economics. Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin, Computer Methods and Programs in Biomedicine, in press. This introduction to wavelet analysis. [32] count the number of permutations (with period-p deliberately avoided) whose periodogram peak at p is larger than that of the time series under test . Home » Book » Wavelet Methods in Statistics. An Introduction to Time Series Analysis and Forecasting: With. Publisher: Cambridge University Press Language: English Format: djvu. In general, exploratory period estimation methods suffer from the developed for short microarray time series, Ptitsyn et al. Wavelet methods for time series analysis Andrew T. Topic: Functional time series analysis, prediction and classification using BAGIDIS. Enquiries: Danie Uys, Tel: 021 808 The method is centered on the definition of a functional, data-driven and highly adaptive semimetric for measuring dissimilarities between curves, typically time series or spectra. Y Zhou, JAD Aston Modeling trigonometric seasonal components for monthly economic time series, Applied Economics, in press. Quantifying uncertainty in change points (2012), Journal of Time Series Analysis, 33:807-823. Dyadic wavelet methods, notably including use of the Haar basis, are of interest as an orthogonal decomposition [25,26], however these can only be applicable to exponential period scales, e.g. 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). ISBN: 0521685087, 9780521685085. 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.