Forecasting non-stationary time series by wavelet process modelling

Forecasting non-stationary time series by wavelet process modelling

Many time series in the applied sciences display a time-varying second order structure. In this article, we address the problem of how to forecast these non-stationary time series by means of non-decimated wavelets. We first consider a model in which only the variance evolves with time. We define a predictor for this model and show an application to the Dow Jones index. Then, we generalise the ...

Forecasting Non - stationary Economic Time Series

Forecasting macroeconomic time series is notoriously difficult. Previously unannounced changes in policy, natural and man-made disasters, institutional changes, new discoveries, new data definitions and revisions among others cause occasional large forecast errors in the standard constant-parameter models. While much effort has been put into working out the theory of estimation, inference and i...

Forecasting Non-Stationary Time Series: From Theory to Algorithms

Generalization bounds for time series prediction and other non-i.i.d. learning scenarios that can be found in the machine learning and statistics literature assume that observations come from a (strictly) stationary distribution. The first bounds for completely non-stationary setting were proved in [6]. In this work we present an extension of these results and derive novel algorithms for foreca...

Using Wavelets and Splines to Forecast Non-Stationary Time Series

 This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...

Modelling Non-Linear and Non-Stationary Time Series