Time series forecasting method based on frequent pattern mining

Abstract

Abstract Pattern Sequence-based Forecasting (PSF) is an effective method for time series prediction. However, the accuracy of this method depends on the selection of parameters such as the length of the pattern sequence and the number of clusters. In diverse time series data sets, these parameters are often priori unknown. This paper innovatively introduces a pattern mining method before the PSF pattern clustering to guide the clustering process and realize the automation of initial parameter selection. Experimental results show that the method proposed in this paper effectively eliminates the uncertainty of PSF caused by the selection of initial parameters. Compared with the original model, it improves the efficiency while ensuring the advantage of prediction accuracy.