Abstract:

To improve the ability to predict future events and effectively address uncertainty, we propose a network architecture based on graph augmentation and attention mechanisms for uncertainty forecasting in multivariate time series. By introducing an implicit graph structure and integrating graph neural network techniques, we capture the mutual dependencies among sequences to model the interactions between time series. We utilize attention mechanisms to capture temporal patterns within the same sequence for modeling the dynamic evolution patterns of time series. We utilize the Monte Carlo dropout method to approximate model parameters and model the predicted sequences as a stochastic distribution, thus achieving accurate uncertainty forecasting in time series. The experimental results indicate that this approach maintains a high level of prediction precision while providing reliable uncertainty estimation, thus providing confidence for use in decision-making tasks.

Key words: uncertainty, graph augmentation, time series, attention mechanism