A dual-topological graph memory network for anti-noise multivariate time series forecasting

Abstract

Multivariate time series (MTS) forecasting plays an essential role in the automation and optimization process of intelligent applications. However, capturing correlations and dependencies among variables in MTS data remains a major challenge for the forecasting models. Although existing methods explain these complex relationships by deeply analyzing variables and intervariable dependencies in MTS data, they tend to be affected by the noise interference prevalent in real-world data, which increases the difficulty for forecasting models to characterize the real features, and thus makes it difficult to achieve satisfactory forecasting results. To address these challenges, this paper designs a dual-topological graph memory (DualGM) network for anti-noise multivariate time series forecasting. The core idea is to analyze the complex relationships in MTS data in detail by constructing dual-topological graphs and to learn and retain the comprehensive information of sequence features by employing a bidirectional self-attention memory module. Specifically, the model first constructs the MTS data as a graph structure and creates a probability topological graph and a distance topological graph based on the similarity between the samples, which helps the model recognize and filter the noise more efficiently in noisy environments, and valuable information is extracted from the noisy data. Then, the topological neighborhood features of the MTS data in each layer are summarized via multilayer graph groups, and the probability and distance spatial information is fused using a bidirectional self-attention mechanism to further enhance the memory and retention of the topological features, thus improving the accuracy of the forecasting model. The experimental results on four real datasets demonstrate that the DualGM model outperforms previously reported methods under various settings; in particular, the RSE values of the DualGM model are higher than those of the second-best results by 12%, 37%, 0.8% and 16% on the four datasets. Moreover, the DualGM model provides a valuable solution for multivariate time series forecasting under noise interference.