Long-Term Multivariate Time-Series Forecasting Model Based on Gaussian Fuzzy Information Granules

Long-term forecasting of multivariate time series has been an important research issue in the field of data mining and knowledge discovery. Fuzzy information granularity is used as an effective tool to handle long-term forecasting of time series. On account of its good interpretability and effect, it has received the attention of more and more scholars. However, although the method has been universally used in univariate time series, its application for multivariate time series has received little attention. In order to utilize the advantages of fuzzy information granularity and fill its gap in solving multivariate time-series forecasting problems. In view of this purpose, we design a long-term multivariate time-series forecasting modeling framework in light of Gaussian fuzzy information granules. The model includes a granulation method of under multivariate time series, as well as a neural network model that combines the backpropagation neural network, long short-term memory neural network, and transformer for long-term prediction, in which there is a fuzzy information granule segmentation method with polynomials as the core line and a new representation method for fuzzy information granules. We carry out experimental evaluations using eight publicly available time-series data, and the results show that our model is able to perform long-term forecasting of multivariate time series with a high satisfactory accuracy.