Multiscale Information Granule-Based Time Series Forecasting Model With Two-Stage Prediction Mechanism

Impressive advancements have been achieved in utilizing information granulation for solving long-term time series prediction problems. However, most state-of-the-art methods suffer from limitations due to not only using the single-scale information granulation but also the lack of trend information. As a result, the prediction models are difficult to capture the multiscale temporal dependencies and dynamic behavior of time series. To address these problems, this article proposes a multiscale information granule-based time series forecasting model. First, the trend-based information granulation strategy is proposed to generate trend information granules that can capture dynamic behavior and trend information in an incremental manner. Then, the multiscale fusion mechanism is proposed to form multiscale information granules with diversified information, which fuses local and global information at different scales. Finally, the two-stage prediction mechanism is proposed to capture multiscale temporal dependencies and perform long-term prediction. A series of experiments were conducted on publicly available time series. Comparative analysis shows that the proposed method outperforms existing numeric models and granular models in long-term prediction on regular and large data time series.