A novel multiscale forecasting model for crude oil price time series

Abstract

Forecasting crude oil prices is an essential research field in the international bulk commodities market. However, price movements present more complex nonlinear behavior due to an increasingly diverse range of risk factors. To achieve better accuracy, this study explores a novel multiscale hybrid paradigm to estimate crude oil prices. The method takes advantage of the variational mode decomposition method to decompose the crude oil price into several simple models, which can be explained using regular factors, irregular factors and trends. Data characteristic analysis is conducted to identify the complexity of different components of the time series. It is important for a multiscale model to select an appropriate model to produce the optimal forecasts. Thus, the final forecasted values are generated by reconstituting all these forecasting items. By investigating the West Texas Intermediate and Brent crude oil prices, this paper presents how data characteristic identification and analysis are conducted in a multiscale paradigm. The empirical analysis proves that the proposed model can achieve superior forecasting results, which indicates the effectiveness of the multiscale model at forecasting complex time series, especially crude oil prices.