scispace - formally typeset
Journal Article

Spectral Analysis and Time Series

Reads0
Chats0
TLDR
In this article, the authors introduce the concept of Stationary Random Processes and Spectral Analysis in the Time Domain and Frequency Domain, and present an analysis of Processes with Mixed Spectra.
Abstract
Preface. Preface to Volume 2. Contents of Volume 2. List of Main Notation. Basic Concepts. Elements of Probability Theory. Stationary Random Processes. Spectral Analysis. Estimation in the Time Domain. Estimation in the Frequency Domain. Spectral Analysis in Practice. Analysis of Processes with Mixed Spectra.

read more

Citations
More filters
Journal ArticleDOI

Forecasting Asymmetric Unemployment Rates

TL;DR: In this article, an out-of-sample forecasting competition is carried out for a set of leading nonlinear time series models and it is shown that several nonlinear forecasts do indeed dominate the linear forecast.
Journal ArticleDOI

Experimentally Confirmed Mathematical Model for Human Control of a Non-Rigid Object

TL;DR: It is demonstrated that the well-known "minimum-jerk" model for smooth reaching movements cannot accomplish the transport of a mass-on-a-spring to a target in an optimally smooth way, and the model extends the concept of smoothness to allow for the control of non-rigid objects.
Journal ArticleDOI

Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion

TL;DR: A method for blind separation of instantaneous mixture of colored sources, based on the minimization a Gaussian mutual information criterion, amounts to jointly approximately diagonalizing a set of estimated spectral density matrices.
Journal ArticleDOI

Goodness of Fit Tests for Spectral Distributions

TL;DR: In this paper, the authors show that the spectral distribution function of a stationary stochastic process standardized by dividing by the variance of the process is a linear function of the autocorrelations.
Journal ArticleDOI

An Application of Nonlinear Time Series Forecasting

TL;DR: In this article, it is shown how ARIMA forecasts can be improved when nonlinearities are present and the autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption.
References
More filters