Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees

Zhi Zhang, Weijian Li, Han Liu

Keywords: Multivariate Time Series Forecasting, Graph Attention Networks, Generalization Error Bound, Rademacher Complexity

Abstract:

Multivariate time series forecasting (MTSF) aims to predict future values of multiple variables based on past values of multivariate time series, and has been applied in fields including traffic flow prediction, stock price forecasting, and anomaly detection. Capturing the inter-dependencies among variables poses one significant challenge to MTSF. Several methods that model the correlations between variables with an aim to improve the test prediction accuracy have been considered in recent works, however, none of them have theoretical guarantees. In this paper, we developed a new norm-bounded graph attention network (GAT) for MTSF by upper-bounding the Frobenius norm of weights in each layer of the GAT model to achieve optimal performance.
Under optimal parameters, we theoretically show that our model can achieve a generalization error bound which is expressed as products of Frobenius norm of weight in each layer and the numbers of neighbors and attention heads, while the latter is represented as polynomial terms with the degree as the number of layers. Empirically, we investigate the impact of different components of GAT models on the performance of MTSF.
Our experiment also verifies our theoretical findings. Empirically, we also observe that the generalization performance of our method is dependent on the number of attention heads, the number of neighbors, the scales (norms) of the weight matrices, the scale of the input features, and the number of layers.
Our method provides novel perspectives for improving the generation performance for MTSF, and our theoretical guarantees give substantial implications for designing attention-based methods for MTSF.

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