基于高斯线性模糊信息粒的新型先知模型用于长期时间序列预测1

Hong Yang, Lina Wang
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引用次数: 0

摘要

本文的重点是如何提高时间序列的预测精度和预测结果的可解释性。首先,针对长期预测提出了一种基于高斯线性模糊近似表示的新型先知模型(GF-Prophet),它能均匀地预测具有一致趋势特征的数据。通过将高斯线性模糊信息颗粒作为输入和输出,GF-Prophet 预测的累积误差明显更小。其次,注意到趋势提取会严重影响预测精度,提出了一种新颖的粒度修正算法,以合并差异不大的相邻信息粒度。这是首次尝试基于模糊信息颗粒建立先知来预测趋势特征。在公共数据集上的实验表明,高斯线性模糊信息颗粒的引入显著提高了传统先知模型的预测性能。与其他经典模型相比,GF-Prophet 不仅具有更高的预测精度,而且具有更好的可解释性,能够清晰地给出投资者实际关注的某一趋势在未来的变化信息、波动幅度和持续时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel Prophet model based on Gaussian linear fuzzy information granule for long-term time series prediction1
The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to.
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