Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties

Understand

Probabilistic forecasting of complex phenomena is paramount to various scientific disciplines and applications.

• Despite the generality and importance of the problem, general mathematical techniques that allow for stable long-term forecasts with calibrated uncertainty measures are lacking.
• For most time series models, the difficulty of obtaining accurate probabilistic future time step predictions increases with the prediction horizon.
• In this paper, we introduce a surprisingly simple approach that characterizes time-varying distributions and enables reasonably accurate predictions thousands of timesteps into the future.