An extensive discussion of the empirical evidence of changes in the time series properties of inflation was provided in Cecchetti, Hooper, Kasman, Schoenholtz, and Watson (2007). In their paper they used an unobserved component model with stochastic volatility to characterize inflation and AR model with time varying coefficients and stochastic volatility to describe the growth of real GDP. Thes...

In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...

A new method for assessing the stationarity of a signal is addressed. The proposed technique is based on the application of time-varying autoregressive models, in which coe cient variations are decomposed upon a set of deterministic basis functions. Stationarity is evaluated by selecting the optimal number of basis functions with a generalized version of Minimum Description Length criterion. Re...

Vector autoregressive (VAR) modelling is one of the most popular approaches in multivariate time series analysis. The parameters interpretation is simple, and provide an intuitive identification of relationships and Granger causality among time series. However, the VAR modelling requires stationarity conditions which could not be valid in many practical applications. Locally stationary or time ...

The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian infere...

It is often needed to label electroencephalogram (EEG) signals by segments of similar characteristics that are particularly meaningful to clinicians and for assessment by neurophysiologists. Within each segment, the signals are considered statistically stationary, usually with similar characteristics such as amplitude and/or frequency. In order to detect the segments boundaries of a signal, we ...