Wavelet analysis and covariance structure of some classes of non-stationary processes
نویسندگان
چکیده
Processes with stationary n-increments are known to be characterized by the stationarity of their continuous wavelet coefficients. We extend this result to the case of processes with stationary fractional increments and locally stationary processes. Then we give two applications of these properties. First, we derive the explicit covariance structure of processes with stationary n-increments. Second, for fractional Brownian motion, the stationarity of the fractional increments of order greater than the Hurst exponent is recovered.
منابع مشابه
Some New Methods for Prediction of Time Series by Wavelets
Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...
متن کاملStructure of Wavelet Covariance Matrices and Bayesian Wavelet Estimation of Autoregressive Moving Average Model with Long Memory Parameter’s
In the process of exploring and recognizing of statistical communities, the analysis of data obtained from these communities is considered essential. One of appropriate methods for data analysis is the structural study of the function fitting by these data. Wavelet transformation is one of the most powerful tool in analysis of these functions and structure of wavelet coefficients are very impor...
متن کاملThe Scale Analysis of Bivariate Non - Gaussian Time
Many scientiic studies require a thorough understanding of the scaling characteristics of observed processes. We derive and justify a decomposition of the usual cross-covariance in terms of scale-by-scale wavelet cross-covariances, and provide an estimator of the wavelet cross-covariance at speciied lag. For jointly stationary but generally non-Gaussian linear processes, asymptotic results are ...
متن کاملWavelet Analysis of Discrete Time Series
We give a brief review of some of the wavelet-based techniques currently available for the analysis of arbitrary-length discrete time series. We discuss the maximal overlap discrete wavelet packet transform (MODWPT), a non-decimated version of the usual discrete wavelet packet transform, and a special case, the maximal overlap discrete wavelet transform (MODWT). Using least-asymmetric or coifle...
متن کاملWavelet Scale Analysis of Bivariate Time Series II: Statistical Properties for Linear Processes
Scaling characteristics of stochastic processes can be examined using wavelet cross-covariances. For jointly stationary but generally non-Gaussian linear processes, the asymptotic properties of the resulting wavelet cross-covariance estimator are derived. The linear processes are assumed to have only a square-summable weight sequence, so that the class of processes includes long-memory processe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017