Memory Augmented Graph Learning Networks for Multivariate Time Series Forecasting

Multivariate time series (MTS) forecasting is a challenging task. In MTS forecasting, We need to consider both intra-series temporal correlations and inter-series spatial correlations simultaneously. However, existing methods capture spatial correlations from the local data of the time series, without taking the global historical information of time series into account. In addition, most methods base on graph neural network mining for the temporal correlations tend to the redundancy of information at adjacent time points in the time-series data, which introduces noise. In this paper, we propose a memory augmented graph learning network (MAGL), which captures the spatial correlations in terms of the global historical features of MTS. Specifically, we use a memory unit to learn from the local data of MTS. The memory unit records the global historical features of the time series, which is used to mine the spatial correlations. We also design a temporal feature distiller to reduce the noise in extracting temporal features. We extensively evaluate our model on four real-world datasets, comparing with several state-of-the-art methods. The experimental results show MAGL outperforms the state-of-the-art baseline methods on several datasets.