Long-Range Persistence in Geophysical Time Series, Volume 40 (Advances in Geophysics)



Publisher: Academic Press

Written in English
Cover of: Long-Range Persistence in Geophysical Time Series, Volume 40 (Advances in Geophysics) |
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Subjects:

  • Earth Sciences,
  • Geophysics,
  • Science,
  • Science/Mathematics,
  • Earth Sciences - Oceanography,
  • Science / Earth Sciences,
  • Science / Oceanography,
  • Earth Sciences - General
  • Edition Notes

    ContributionsRenata Dmowska (Editor), Barry Saltzman (Editor)
    The Physical Object
    FormatHardcover
    Number of Pages175
    ID Numbers
    Open LibraryOL9279769M
    ISBN 100120188406
    ISBN 109780120188406

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 .

Long-Range Persistence in Geophysical Time Series, Volume 40 (Advances in Geophysics) Download PDF EPUB FB2

Search in this book series. Long-Range Persistence in Geophysical Time Series. Edited by Renata Dmowska, Barry Saltzman. Vol Pages iii-xi, () Download full volume. Previous volume. Next volume. Actions for selected chapters. Select all /. This volume explores in detail one of the main components of noise, that of long-range persistence or memory.

The first chapter is a broad summary of theory and techniques of long-range persistence in time series; the second chapter is the application of long-range persistence to a variety of geophysical time series. This volume explores in detail one of the main components of noise, that of long-range persistence or memory.

The first chapter is a broad summary of theory and techniques of long-range persistence in time series; the second chapter is the application of long-range persistence to a variety of geophysical time : Renata Dmowska.

This volume explores in detail one of the main components of noise, that of long-range persistence or memory. The first chapter is a broad summary of theory and techniques of long-range persistence in time series; the second chapter is the application of long-range persistence to a variety of geophysical time : $ We finish this paper by discussing long-range persistence of three geophysical time series-palaeotemperature, river discharge, and Auroral electrojet index-with the three representing three.

Time series in the Earth Sciences are often characterized as self-affine long-range persistent, where the power spectral density, S, exhibits a power-law dependence on frequency, f, S(f) ~ f - β, with β the persistence strength. For modelling purposes, it is important to determine the strength of self-affine long-range persistence β as precisely as possible and to quantify the.

Three examples of geophysical time series exhibiting long-range persistence. a Bi-decadal oxygen isotope data set δ 18 O (proxy for palaeotemperature) from Greenland Ice Sheet Project Two (GISP2) for the l years (Stuiver et al. ), with values given at 20 year intervals. b Discharge of the Elkhorn river (at Waterloo, Nebraska, USA) sampled daily for the period from 01.

Time series methods are essential tools in the analysis of many geophysical systems. This volume, which consists of papers presented by a select, international group of statistical and geophysical experts at a Workshop on Time Series Analysis and Applications to Geophysical Systems at the Institute for Mathematics and its Applications (IMA) at the University of Minnesota from November Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning.

We conclude that the presence of long-range persistence has a significant effect on the likelihood of severe drought. The presence of long-range persistence does not, however, appear to increase the ability to predict future climatological and hydrological time series to any significant degree (Noakes et al., ).

Annette Witt, Bruce D. Malamud, Quantification of Long-Range Persistence in Geophysical Time Series: Conventional and Benchmark-Based Improvement Techniques, Surveys in Geophysics, /s, 34, 5, (), ().

The basic characteristic of a self-affine time series is that the persistence is scale invariant. Thus, a self-affine time series has a long-range persistence and these are found in a wide variety.

The Hurst phenomenon is a well-known feature of long-range persistence first observed in hydrological and geophysical time series by E. Hurst in the s. It has also been found in several cases in turbulence time series measured in the wind tunnel, the atmosphere, and in rivers.

Here, we conduct a systematic investigation of the value of the Hurst coefficient H in atmospheric surface-layer. (Advances in Geophysics 40) Renata Dmowska and Barry Saltzman (Eds.)-Long-Range Persistence in Geophysical Time Series-Elsevier, Academic Press ().

Annette Witt, Bruce D. Malamud, Quantification of Long-Range Persistence in Geophysical Time Series: Conventional and Benchmark-Based Improvement Techniques, Surveys in Geophysics, /s, 34, 5, (), ().

Long-range correlation (also known as long-term memory or persistence) refers to the correlation functions decaying exponentially over time and keeping a significant value over a wide temporal range (Kantelhardt et al. ; Leung ). In another words, long-range correlation means that if an anomaly of a particular sign exists in the past.

Financial and geophysical data, like many other low and high frequency time series, are known to exhibit some memory effects. These memory effects may be long or short, permanent or temporal depending on the event that is being modeled.

The purpose of this study is to investigate the memory effects characterized by the financial market closing values and volcanic eruption time series as well. Long-range correlation (also known as long-term memory or persistence) refers to the correlation functions decaying exponentially over time and keeping a significant value over a wide temporal range (Kantelhardt et al.

; Leung ).In another words, long-range correlation means that if an anomaly of a particular sign exists in the past, it will most likely continue to exist in the future.

Findings from the numerical simulations confirm the existence of long-range persistence (long-memory behavior) in both the financial and geophysical time series. Furthermore, the numerical results from this study indicates an approximate inverse relationship between the parameter of the Lévy process and the scaling parameters of the DFA and.

"The book Time Series Analysis and Inverse Theory for Geophysicists by D. Gubbins is according to the author, aimed at "providing the students of geophysics with an introduction to these [digital] techniques and an understanding of the underlying philosophy and mathematical theory." My impression is that the author has achieved this goal quite Reviews: 3.

Quantification of Long-Range Persistence in Geophysical Time Series: Conventional and Benchmark-Based Improvement Techniques Annette Witt and Bruce D. Malamud 4 August | Surveys in Geophysics, Vol. 34, No. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model.

All models are applied to a time series of mean daily discharge exhibiting long range dependence. or short-term persistence. However, a given value may also be correlated with distant neighbors in the time series as well. When there is significant correlation between val-ues that are far apart relative to the sampling interval, then long-range correlation or long-range persistence (LRP) enters the picture.

LRP reflects long-term memory and the. An important topic in the study of the time series behavior and, in particular, meteorological time series is the long-range dependence.

This paper explores the behavior of rainfall variations in different periods, using long-range correlations analysis. Semivariograms and Hurst exponent were applied to historical data in different pluviometric stations of the Río Bravo-San Juan watershed.

Tinsley B A and Yu F Solar Variability and its Effects on Climate (Geophysical Monograph Series vol ) ed J Pap (Washington, DC: AGU) p (doi/GM) Crossref Torrence C and Compo G P A practical guide to wavelet analysis Bull. Meteorol. Additional exercises can be used in a classroom setting.

A Web site offers access to the time series and wavelets used in the book, as well as information on accessing software in S-Plus and other languages. Students and researchers wishing to use wavelet methods to analyze time series will find this book. Long-range dependence (LRD), also called long memory or long-range persistence, is a phenomenon that may arise in the analysis of spatial or time series data.

It relates to the rate of decay of statistical dependence of two points with increasing time interval or spatial distance between the points.

A phenomenon is usually considered to have long-range dependence if the dependence decays more. Abstract: Geophysical time series have a complex nature that poses challenges to reaching assertive conclusions, and require advanced mathematical and computational tools to unravel embedded information.

In this paper, time–frequency methods and hierarchical clustering (HC) techniques are combined for processing and visualizing tidal information.

Then, we study the asymptotic behavior of the tapered periodogram of long range dependent time series for frequencies near the origin, and we obtain the asymptotic distribution of the log-periodogram estimate for possibly non-Gaussian observation when the tapered periodogram is used. The Hurst–Kolmogorov (HK) behavior in geophysical series is the product of fluctuations occurring simultaneously at several time scales.

In streamflow time series, the clustering of similar events characterizes this behavior and brings forth the peculiarly persistent structure of such series. This paper considered estimation of long-range parameters of a seasonal model using regression approach.

Multiple linear regression model was deduced from SARIMA (5, 0, 0)x(0, 1, 0) 4 model. The data used were quarterly data of Nigerian gross domestic products from toCBN Statistical Bulletin, (x10 6).

The Multiple linear regression model was fitted to the data, and the.[1] Geophysical time series sometimes exhibit serial correlations that are stronger than can be captured by the commonly used first‐order autoregressive model.

In this study we demonstrate that a power law statistical model serves as a useful upper bound for the persistence of total ozone anomalies on monthly to interannual timescales. Such a.