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

Artifact areas were present in the signals, potentially because of contact and other sensing. 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. The obtained results are very similar. 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. Two principally independent methods of time series analysis are used: the T-R periodogram analysis (both in the standard and “scanning window” regimes) and the wavelet-analysis. Venue: Statistics Building (c/o Victoria- and Bosman streets, Stellenbosch), Room 2021. Then they construct an ``F-index'' structure with an R*-tree --- a tree-indexing method for spatial data. Topic: Functional time series analysis, prediction and classification using BAGIDIS. Thus, a wide class of analyses of relevance to geophysics can be undertaken within this framework. This gives a method for systematically exploring the properties of a signal relative to some metric or set of metrics. In this paper, classical surrogate data methods for testing hypotheses concerning nonlinearity in time-series data are extended using a wavelet-based scheme. They justify keeping the first . Random number generation; Calculations on statistical data; Correlation and regression analysis; Multivariate methods; Analysis of variance and contingency table analysis; Time series analysis; Nonparametric statistics. Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Siebes, "The haar wavelet transform in the time series similarity paradigm," in PKDD '99: Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery, (London, UK), pp. Similarity search,; time series analysis. Bullmore E, Long C, Suckling J, Fadili J, Calvert G, Zelaya F, Carpenter TA, Brammer M.