Data for: On the predictability of energy commodity markets by an entropy-based computational method

Abstract of associated article: This paper proposes a novel computational method for assessing the predictability of commodity market time series, by predicting the entropy of the series under investigation. Assessing the predictability of a time series is the first mandatory step in order to further apply low-risk and efficient price forecasting methods. According to conventional entropy-based analysis (where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict (i.e. ex-ante) the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, usually used in many computational aspects of management science. Preliminary results, applied to energy commodity futures, show the effectiveness of the proposed method for application to energy market time series.

关联论文摘要：本文提出一种新颖的计算方法，用于评估商品市场时间序列的可预测性，具体通过对研究目标序列的熵（entropy）进行预测实现。对时间序列的可预测性开展评估，是后续应用低风险、高效价格预测方法的首要必要步骤。传统基于熵的分析（其中熵始终通过事后（ex-post）估计得到）表明，高熵值对应不可预测序列，而稳定性更强的序列则拥有更低的熵值。本文基于历史数据观测，对序列未来行为的熵值进行事前（ex-ante）预测。我们的预测采用最优最小二乘最小化算法，该算法广泛应用于管理科学的诸多计算场景。针对能源商品期货开展的初步实验结果显示，本文所提方法在能源市场时间序列场景中具备良好的应用有效性。