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Multivariate time series prediction has attracted a lot of attention becauseof its wide applications such as intelligence transportation, AIOps. Generativemodels have achieved impressive results in time series modeling because theycan model data distribution and take noise into consideration. However, manyexisting works can not be widely used because of the constraints of functionalform of generative models or the sensitivity to hyperparameters. In this paper,we propose ScoreGrad, a multivariate probabilistic time series forecastingframework based on continuous energy-based generative models. ScoreGrad iscomposed of time series feature extraction module and conditional stochasticdifferential equation based score matching module. The prediction can beachieved by iteratively solving reverse-time SDE. To the best of our knowledge,ScoreGrad is the first continuous energy based generative model used for timeseries forecasting. Furthermore, ScoreGrad achieves state-of-the-art results onsix real-world datasets. The impact of hyperparameters and sampler types on theperformance are also explored. Code is available athttps://github.com/yantijin/ScoreGradPred.

ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models