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Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

机译：基于小波熵的原油价格动态分析与预测

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For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.
机译：对于复杂的和非线性的原油价格动态和运动建模，小波分析可以分解时间序列，并根据小波族和分解规模的不同假设，产生多个具有经济意义的分解结构。但是，确定最佳模型规格将严重影响预测准确性。在本文中，我们提出了一种新的基于小波熵的方法来确定最佳模型规格并构建有效的基于小波熵的预测模型。引入小波熵算法确定最优的小波族和分解尺度，从而提高预测性能。在原油市场上进行的经验研究表明，就用于模型预测准确性的常规性能评估标准而言，该算法优于基准模型。

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