Abstract
We propose a nonparametric spatio-temporal stochastic differential equation (SDE) model that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We augment the input space of the drift function of an SDE with a temporal component to account for spatio-temporal patterns. The experiments on a real world data set demonstrate that the spatio-temporal model is better able to fit the data than the spatial model and also reduce the forecasting error.
| Original language | English |
|---|---|
| Title of host publication | NIPS 2018 Spatiotemporal Workshop |
| Subtitle of host publication | 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada |
| Publisher | Neural Information Processing Systems Foundation |
| Pages | 1-5 |
| Publication status | Published - 2018 |
| MoE publication type | D3 Professional conference proceedings |
| Event | NIPS Spatiotemporal Workshop - Montreal, Canada Duration: 3 Dec 2018 → 8 Dec 2018 |
Workshop
| Workshop | NIPS Spatiotemporal Workshop |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 03/12/2018 → 08/12/2018 |
Keywords
- stochastic differential equation
- gaussian processes
- spatiotemporal drift