| Autor: |
Rasul, Kashif, Seward, Calvin, Schuster, Ingmar, Vollgraf, Roland |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8857-8868, 2021 |
| Druh dokumentu: |
Working Paper |
| Popis: |
In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this end, we use diffusion probabilistic models, a class of latent variable models closely connected to score matching and energy-based methods. Our model learns gradients by optimizing a variational bound on the data likelihood and at inference time converts white noise into a sample of the distribution of interest through a Markov chain using Langevin sampling. We demonstrate experimentally that the proposed autoregressive denoising diffusion model is the new state-of-the-art multivariate probabilistic forecasting method on real-world data sets with thousands of correlated dimensions. We hope that this method is a useful tool for practitioners and lays the foundation for future research in this area. |
| Databáze: |
arXiv |
| Externí odkaz: |
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