利用代谢DWT和MWA-GM预测股票指数(1,1)

Ziyang Jiang, Siyuan Lu, Jun Lin, Zhongfeng Wang
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引用次数: 0

摘要

股票指数预测是现代金融时间序列分析的重要任务之一,高效的预测算法是研究人员和投资者所需要的。本文提出了一种新颖有效的模型来提高预测精度。我们首先设计了代谢离散小波变换(DWT)和移动加权平均灰色模型(MWA-GM(1,1)),两者都能灵活地从最新数据中捕获更多有用的信息。通过对这两部分的整合,混合模型不仅对各种股票指数的预测具有较强的适应性,而且具有较高的预测精度。与GM(1,1)模型相比,我们的混合模型在预测全球8个代表性股指时,误差降低了17%以上。同时,我们的方法也明显优于其他基于gm的预测方法,为将DWT和灰色模型相结合进行时间序列预测提供了很好的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Stock Indexes with Metabolic DWT and MWA-GM(1,1)
Predicting stock indexes is one of the most important tasks of modern financial time series analysis, and efficient prediction algorithms are desired by researchers and investors. This work proposes a novel and effective model to improve the forecasting precision. We first design a metabolic discrete wavelet transform (DWT) and a moving weighted average grey model (MWA-GM(1,1)), both of them could capture more useful information from the latest data flexibly. By integrating these two components, the hybrid model not only has strong adaptability in the prediction of various stock indexes, but also has high prediction accuracy. Compared to GM(1,1) model, our hybrid model reduces error by more than 17% when predicting eight representative stock indexes globally. Meanwhile, our method also significantly outperforms other GM-based prediction methods, providing a good idea of combining DWT and grey model to predict time series.
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