Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting

Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting

Longyuan Li, Junchi Yan, Xiaokang Yang, Yaohui Jin

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2901-2908. https://doi.org/10.24963/ijcai.2019/402

Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner.
Keywords:
Machine Learning: Time-series;Data Streams
Uncertainty in AI: Uncertainty in AI
Machine Learning: Probabilistic Machine Learning
Machine Learning: Interpretability