基于统计特征搜索的多元时间序列预测方法

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.