Information Granule-Based Time Series Prediction via Synergizing Multiscale and Multitype Information Granulations

Remarkable achievements have been made in utilizing information granulation for improving the accuracy and interpretability of time series forecasting. However, existing information granule-based methods suffer from multiple limitations, including the loss of scale information and the neglect of nonlinear trends and magnitude information, which compromise prediction quality. To address these issues, this article proposes an information granule-based time series prediction method by synergizing multiscale and multitype information granulations. Firstly, an adaptive multiscale sequence generation method is designed to generate the multiscale subsequences, which provide the multiscale views of time series. Then, the dual-mode granulation mechanism integrating magnitude-type and trend-type information granulations is proposed to comprehensively exploit linear trends, non-linear trends, and magnitude information of time series. Thirdly, the cross-scale information granule fusion is designed to model the inherent interrelationships between multiscale information granules, which can promote the collaboration across different-scale information granules. Finally, multitype information granules are synergized to perform prediction, and the prediction process also benefits from the fused multiscale information. Experiments on small, medium, and large size time series datasets demonstrate that the proposed method achieves significant performance compared with the state-of-the-art methods.