Forecasting of Crude Oil Prices Using Wavelet Decomposition Based Denoising with ARMA Model

Abstract

The uncertainty caused by high volatile crude oil prices and the higher level of deregulations worldwide has significant effects on the economic growth of a country. The financial markets of many developing countries experienced a severe downturn during the oil price shocks in March-April 2020. Traditional predictive approaches, which assume linearity and stationarity of time series in the long run, fail to accurately capture short-term fluctuations. This paper presents an efficient algorithm based on ARMA denoising and taking advantage of the wavelet transformation. By decomposing the time series and extracting the intricate underlying structure, wavelet denoising minimizes distortions and enhances forecasting accuracy. The results demonstrate a substantial improvement in performance compared to conventional forecasting techniques.