Multi-Stage Optimization Filter for Trend Based Short-Term Forecasting

A new method is proposed to estimate the long-term seasonal component by a multistage optimization filter with a leading phase shift (MOPS). It can be utilized to provide better predictions in case of the seasonal component autoregressive (SCAR) model, where data are filtered/decomposed into trend and remainder components and then forecasts for constituent components generated separately and later combined. This reinforces the importance of trend estimation filtering/decomposition methods, which are scarce and only few methods, primarily wavelet decomposition, have improved upon the forecasts generated by statistical linear models. We contribute to the literature by introducing a new trend estimation method, and the forecast results are compared with the most popular trend estimation methods, such as frequency filters, wavelet decomposition, empirical mode decomposition (EMD), and Hodrick–Prescott (HP) filter, through their performance in generating short-term forecasts for day-ahead electricity prices. Our method for trend estimation performs better in terms of providing short-term forecasts as compared with some well-known methods, and the best forecast, according to the Diebold and Mariano (1995) test, is obtained by using our MOPS filter with annual trend period length.