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 using splines based on this procedure. This method is applied to forecast simulated data and electricity load consumption of two regions. Result of the study show, the proposed method performance is better than the local polynomial fitting.