In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete imag...

A key application of long memory time series models concerns innation. Long memory implies that shocks have a long-lasting eeect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for innation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (...

ARFIMA is a time series forecasting model, which is an improve d ARMA model, the ARFIMA model proposed in this article is d emonstrated and deduced in detail. combined with network traffi c of CERNET backbone and the ARFIMA model,the result sho ws that,compare to the ARMA model, the prediction efficiency a nd accuracy has increased significantly, and not susceptible to sa mpling.

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accur...

This correspondence addresses the asymptotic normal distribution of the sample mean and the sample covariance matrix of mixed spectra time series containing a sum of sinusoids and a moving average (MA) process. Two central limit (CL) theorems are proved. As an application of this result, the asymptotic normal distribution of any sinusoidal frequencies estimator of such time series based on seco...