Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.

ABSTRACT: In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study causal discovery and forecasting for nonstationary time series. By exploiting a particular type of state-space model to represent the processes, we show that nonstationarity helps to identify causal structure and that forecasting naturally benefits from learned causal knowledge. Specifically, we allow changes in both causal strengths and noise variances in the nonlinear state-space models, which, interestingly, renders both the causal structure and model parameters identifiabl

SUBMITTER: Huang B 

PROVIDER: S-EPMC6730644 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature