Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecasting
Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures excel at sequence modeling, current probabilistic approaches typically rely on restrictive parametric likelihoods or quantile-based objectives, thereby limiting their ability to capture complex joint distributions in correlated time series. To overcome these limitations, we propose \textit{Enformer} and its spatiotemporal extension, \textit{GEnformer}. These models synthesize the expressive power of Transformers with engression, a stochastic learning paradigm for modeling conditional distributions. By injecting stochastic noise and optimizing a...
| Authors | Rajdeep Pathak, Rahul Goswami, Madhurima Panja et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 20 June 2026 |
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