Get this from a library! Time series analysis and applications to geophysical systems. [David R Brillinger; Enders A Robinson; Frederic Paik Schoenberg;] -- "The works in this volume deal with the theoretical and methodological issues as well as real geophysical applications, and are written with both statistical and geophysical audiences in mind. Nowadays, there is evidence that hydrological processes exhibit long-range dependence (LRD), i.e. power-type decay of autocorrelation also known as the Hurst phenomenon. This means that the stationarity assumption of hydrological time series, which has been widely used in the past, cannot be further advocated. The objective of this paper is to detect the long-range dependence in rainfall in. Get this from a library! Time Series Analysis and Applications to Geophysical Systems: Part I. [David R Brillinger; Enders Anthony Robinson; Frederic Paik Schoenberg] -- Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on. Aldrich, E. () Wavelets: A package of functions for computing wavelet filters, wavelet transform and multiresolution analyses.R package version Biswas, A. () Landscape characteristics influence the spatial pattern of soil water storage: Similarity over times and at Bloomfield, P. () Fourier analysis of time series: An introduction. 2nd ed.,

The essence of Hurst's observations were that after examining numerous geophysical time series throughout the world (annual streamflow volumes, rainfall, lake varves, etc.), he determined that the degree of apparent persistence (long intervals of well below or well above "normal" trends) could be indexed to a coefficient "H" which we now know. A time series is broadly defined as any series of measurements taken at different times. Some basic descriptive categories of time series are 1) long vs short, 2) even time-step vs uneven time-step, 3) discrete vs continuous, 4) periodic vs aperiodic, 5) stationary vs nonstationary, and 6) . In the paper, a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology. The detrended fluctuation analysis (DFA) method is powerfully used to reveal the extent of long-range correlations in time series [18,19]. It can filter out the trend variation first and then disclose the persistence characteristics of a time series.

"Testing for a break in persistence under long‐range dependencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages , May. Sibbertsen, Philipp & Kruse, Robinson, " Testing for a break in persistence under long-range dependencies," Hannover Economic Papers (HEP) dp, Leibniz Universität Hannover. The existence of this cycle provides a basis for long-range climate forecasting over the western United States at decadal time scales. 17 refs., 5 figs. DOI: /science Building America Best Practices Series Volume Builders Challenge Guide to 40% Whole-House Energy Savings in the Cold and Very Cold Climates. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages , July. Luca Benati, " Investigating Inflation Persistence Across Monetary Regimes," The Quarterly Journal of Economics, Oxford University Press, vol. (3), pages We outline an efficient integrated wavelet, a spectral, and a cross‐spectral approach for the time‐series analysis of geologic data. Here these techniques are applied to a database of large igneous provinces (LIPs) in order to test for cycles, trends, and abrupt changes in .