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Non-linear and non-stationary time series analysis

M. B. Priestley
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The article was published on 1988-01-01 and is currently open access. It has received 1255 citations till now. The article focuses on the topics: Order of integration & Cross-spectrum.

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Journal ArticleDOI

Introduction to Time Series and Forecasting.

Peter J. Brockwell, +1 more
- 01 Sep 1998 - 
TL;DR: A general approach to Time Series Modelling and ModeLLing with ARMA Processes, which describes the development of a Stationary Process in Terms of Infinitely Many Past Values and the Autocorrelation Function.
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Introduction to time series and forecasting

Peter J. Brockwell, +1 more
TL;DR: In this paper, the authors present a general approach to time series analysis based on simple time series models and the Autocorrelation Function (AFF) and the Wold Decomposition.
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Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals

Boualem Boashash
TL;DR: The concept of instantaneous frequency (IF), its definitions, and the correspondence between the various mathematical models formulated for representation of IF are discussed in this paper, and the extent to which the IF corresponds to the intuitive expectation of reality is also considered.
Journal ArticleDOI

A new view of nonlinear water waves: the Hilbert spectrum

Norden E. Huang, +2 more
- 01 Jan 1999 - 
TL;DR: In this paper, Hilbert spectral analysis is proposed as an alternative to wavelet analysis, which provides not only a more precise definition of particular events in time-frequency space, but also more physically meaningful interpretations of the underlying dynamic processes.
Journal ArticleDOI

Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

Timo Teräsvirta
- 01 Mar 1994 - 
TL;DR: In this article, the authors consider the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) models, and consider the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models.