基于统计特征搜索的多元时间序列预测方法

Statistical Feature-based Search for Multivariate Time Series Forecasting

时间序列中包含一些长期依赖关系,如长期趋势性、季节性和周期性,这些长期依赖信息的跨度可能是以月为单位的,直接应用现有方法无法显式建模时间序列的超长期依赖关系。该文提出基于统计特征搜索的预测方法来显式地建模时间序列中的长期依赖。首先对多元时间序列中的平滑特征、方差特征和区间标准化特征等统计特征进行抽取,提高时间序列搜索对趋势性、周期性、季节性的感知。随后结合统计特征在历史序列搜索相似的序列,并利用注意力机制融合当前序列信息与历史序列信息,生成可靠的预测结果。在5个真实的数据集上的实验表明该文提出的方法优于6种最先进的方法。

There are long-term dependencies,such as trends,seasonality,and periodicity in time series,which may span several months.It is insufficient to apply existing methods in modeling the long-term dependencies of the series explicitly.To address this issue,this paper proposes a Statistical Feature-based Search for multivariate time series Forecasting(SFSF).First,statistical features which include smoothing,variance,and interval standardization are extracted from multivariate time series to enhance the perception of the time series’trends and periodicity.Next,statistical features are used to search for similar series in historical sequences.The current and historical sequence information is then blended using attention mechanisms to produce accurate prediction results.Experimental results show that the SFSF method outperforms six state-of-the-art methods.