Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods.