A generalized pattern matching approach for multi-step prediction of crude oil price
A generalized pattern matching approach for multi-step prediction of crude oil price
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DOI：
10.1016/j.eneco.2006.10.012
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发表时间：
2008-05
期刊：
影响因子：
12.8
通讯作者：
Ying Fan;Qian Liang;Yi-Ming Wei
中科院分区：
文献类型：
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作者：
Ying Fan;Qian Liang;Yi-Ming Wei
将模式匹配技术应用于原油价格的多步预测，提出了一种新的基于遗传算法的广义模式匹配方法(GPMGA)，该方法可以根据历史观测对未来的原油价格进行预测。这种方法可以从历史数据中发现当代原油价格最相似的模式。基于类似的历史模式，可以计算出对未来原油价格的多步预测。在GPMGA建模过程中，没有直接采用传统的模式匹配方法。将历史数据在x轴和y轴方向上变换到更大或更小的尺度，从而可以得到反映当前价格变动的广义价格模式。这种处理克服了传统的模式识别系统建模方法(PMRS)的局部缺陷，并且可以在更大的模式规模中找到匹配的历史模式。由于该方法既考虑了历史相似性，又考虑了差异性，因此提出了“广义模式匹配”的概念。它通过各种变换找出更多本质上的相似性，为多步预测提供了新的基础。对布伦特原油和西德克萨斯中质原油价格进行了一个月的预测，并进行了相关的实证研究，取得了满意的预测结果。最后，通过与PMRS、Elman网络等其他时间序列预测方法的比较，验证了GPMGA的有效性和优越性。
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.