基于改进的数据变异模糊关系建立时间序列预测模型

Ha Che-Ngoc, Luan Nguyenhuynh, Dan Nguyen-Thihong, Tai Vo-Van
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引用次数: 3

摘要

时间序列预测一直是统计学家和数据科学家感兴趣的问题,因为它在现实中提供了很多好处。本文提出的模糊时间序列模型既能对历史数据进行插值,又能对未来进行有效预测。首先,我们根据原始数据变化的百分比构建通用集,并通过开发的自动算法将其划分为合适数量的聚类;其次,建立序列中各元素与得到的聚类之间新的模糊关系。变异越大,聚类越分裂。最后,结合上述两种改进,我们提出了预测未来的新原则。在包括3003系列m3竞争数据在内的多个知名数据集上的实验表明,与现有模型相比,本文提出的模型具有突出的优势。由于该模型是通过Matlab程序建立的,因此可以有效地应用于实序列。
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Building the Forecasting Model for Time Series Based on the Improved Fuzzy Relationship for Variation of Data
Forecasting for time series has always been of interest to statisticians and data scientists because it offers a lot of benefits in reality. This study proposes the fuzzy time series model which can both interpolate historical data, and forecast effectively for the future with the important contributions. First, we build the universal set based on the percentage of the original data variation, and divide it to clusters with the suitable number by the developed automatic algorithm. Next, the new fuzzy relationship between each element in series and the obtained clusters is established. The bigger the variation is, the more the clusters are divided. Finally, combining the two above improvements, we propose the new principle to forecast for the future. The experiments on many well-known data sets, including 3003 series of M3-competition data show that the proposed model has shown the outstanding advantage in comparing to the existing ones. Because the proposed model is established by the Matlab procedure, it can apply effectively for real series.
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