Crude Oil Price Forecasting: A Transfer Learning Based Analog Complexing Model

Abstract

Most of the existing models for oil price forecasting only use the data in the forecasted time series itself. This study proposes a transfer learning based analog complexing model (TLAC). It first transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by analog complexing method. Finally, genetic algorithm is introduced to find the optimal matching between the two important parameters in TLAC. Two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price are used for empirical analysis, and the results show the effectiveness of the proposed model.