Wavelet methods for time series analysis by Andrew T. Walden, Donald B. Percival

Wavelet methods for time series analysis

Download eBook

Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival ebook
Format: djvu
Publisher: Cambridge University Press
Page: 611
ISBN: 0521685087, 9780521685085

[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 . In this paper, classical surrogate data methods for testing hypotheses concerning nonlinearity in time-series data are extended using a wavelet-based scheme. 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. ISBN: 0521685087, 9780521685085. Wavelet methods for time series analysis Andrew T. In a previous post we introduced the problem of detecting Gravity Waves using Machine Learning and suggested using techniques like Minimum Path Basis Pursuit. This gives a method for systematically exploring the properties of a signal relative to some metric or set of metrics. Here, we drill down into the theoretical For example, many images are S- sparse in a wavelet basis; this is the basis of the newer JPEG2000 algorithm. This allows us to reconstruct a signal with as few . Thus, a wide class of analyses of relevance to geophysics can be undertaken within this framework. Publisher: Cambridge University Press Language: English Format: djvu. 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. Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. In general, exploratory period estimation methods suffer from the developed for short microarray time series, Ptitsyn et al. Topic: Functional time series analysis, prediction and classification using BAGIDIS. Venue: Statistics Building (c/o Victoria- and Bosman streets, Stellenbosch), Room 2021.