摘要翻译：
本文提出了一种基于多层前馈神经网络的原油现货价格短期预测模型。寻找最优的神经网络模型结构受到了广泛的关注。此外，还对数据预处理的几种方法进行了试验。我们的方法是根据预处理现货价格的滞后值创建一个基准，然后将到期前1个月、2个月、3个月和4个月的预处理期货价格一个一个地添加进来。在基准上的结果表明，13个滞后的动态模型是短期预测现货价格方向的最优模型。对未来一天、两天和三天市场走势的预测准确率分别为78%、66%和53%。所有的实验，包括期货数据作为输入，结果表明，在短期内，期货价格确实包含了现货价格方向的新信息。所得结果将有助于投资者和个人对原油动态的全面了解，从而为风险管理提供帮助。
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英文标题：
《Forecasting Model for Crude Oil Price Using Artificial Neural Networks
  and Commodity Futures Prices》
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作者：
Siddhivinayak Kulkarni, Imad Haidar
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
  This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the crude oil dynamic which help investors and individuals for risk managements.
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PDF链接：
https://arxiv.org/pdf/0906.4838