A generalized pattern matching approach for multi-step prediction of crude oil price

This paper applies pattern matching technique to multi-step prediction of crude oil prices and proposes a new approach: generalized pattern matching based on genetic algorithm (GPMGA), which can be used to forecast future crude oil price based on historical observations. This approach can detect the most similar pattern in contemporary crude oil prices from the historical data. Based on the similar historical pattern, a multi-step prediction of future crude oil prices can be figured out. In GPMGA modeling process, the traditional pattern matching is not directly employed. Historical data is transformed to larger or smaller scales in the x-axis and the y-axis directions, so that a generalized price pattern reflecting current price movement can be obtained. This treatment overcomes the local deficiency of the traditional pattern modeling in recognition system approach (PMRS), and in addition to this, a matched historical pattern in a larger pattern size can be found. Since the approach takes not only historical similarities but also differences into account, the concept of “generalized pattern matching” is proposed here. It proves a new basis for multi-step prediction by finding out more essential similarities through various transformations. The related empirical study is constructed for a one-month forecasting of the Brent and WTI crude oil prices, and satisfying forecasting results are attained. At the end, comparisons with some other time series prediction approaches, such as PMRS and Elman network, demonstrate the effectiveness and superiority of GPMGA over others.