Information granules-based long-term forecasting of time series via BPNN under three-way decision framework

Abstract

As a significant issue in the machine learning field, the long-term forecasting of time series has aroused extensive attention from academia and industry. Specifically, transforming time series into granularity time series (GTS) for forecasting is usually perceived as an efficient way to address the long-term forecasting of time series. Although some long-term forecasting models have been explored to granulate time series information, the extraction of time series trend information is always accompanied by information losses. In order to address this challenge, a Back Propagation neural network (BPNN) time series long-term forecasting model is established via information granules under the framework of three-way decisions (TWD), which is conducive to greatly preserving the trend information of time series. First, significant points in the time series are extracted to initially extract the trend information of time series. Second, a reasonable loss function and conditional probability of TWD are defined, and a TWD-based method is proposed to compress the information and extract the trend information. Third, information granules are used as input for the prediction via BPNN. Notably, the proposed model not only retains the trend information, but also reduces errors due to the segmentation of information particles and extends the application range of forecasting problems with TWD. Finally, experimental outcomes with several data sets reveal that the proposed model owns better performance than existing long-term forecasting models.