  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.
