Multivariate time series prediction has attracted a lot of attention because of its wide applications such as intelligence transportation, AIOps. Generative models have achieved impressive results in time series modeling because they can model data distribution and take noise into consideration. However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters. In this paper, we propose ScoreGrad, a multivariate probabilistic time series forecasting framework based on continuous energy-based generative models. ScoreGrad is composed of time series feature extraction module and conditional stochastic differential equation based score matching module. The prediction can be achieved by iteratively solving reverse-time SDE. To the best of our knowledge, ScoreGrad is the first continuous energy based generative model used for time series forecasting. Furthermore, ScoreGrad achieves state-of-the-art results on six real-world datasets. The impact of hyperparameters and sampler types on the performance are also explored. Code is available at https://github.com/yantijin/ScoreGradPred.

翻译：多变时间序列预测由于其广泛的应用,例如情报传输、AIOps等,引起了人们的极大关注。生成模型在时间序列建模方面取得了令人印象深刻的成果,因为它们可以模拟数据分布和考虑到噪音。然而,由于基因模型功能形式的限制或对超参数的敏感度,许多现有工程无法广泛使用。在本文中,我们提议了基于连续基于能源的基因化模型的多变概率时间序列预测框架“计分格拉德”。评分格拉德由时间序列提取模块和基于条件的随机随机差异方程匹配模块组成。该预测可以通过迭代解决反向时间 SDE 匹配模块实现。据我们所知,CondGrad是用于时间序列预测的第一个基于连续能量的基因化模型。此外,CowGrad在六个真实世界数据集上取得了最新的结果。还探讨了超参数和采样器类型对性能的影响。代码可在https://github.com/yantijin/ScoreGradpred上查阅。