为提升对未来事件的预判能力并有效应对不确定性,提出了一种基于图增强和注意力机制的网络架构,用于多元时间序列的不确定性预测.通过引入隐含式图结构并结合图神经网络技术,捕捉各序列间相互依赖关系,从而建模时间序列之间的相互影响;运用注意力机制捕捉同一序列内的时序变化模式,以建模时间序列的动态演变规律;采用蒙特卡洛随机失活(Monte Carlo dropout)方法近似模型参数,并将预测序列建模为随机分布,以实现精确的时间序列不确定性预测.实验证明,该方法在保持较高预测精度的同时,还能进行可靠的不确定性估计,可以为决策任务提供置信度信息.
Time series uncertainty forecasting based on graph augmentation and attention mechanism
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.
万方数据
维普
