Shengbin Nie

Disentangling Coarse and Fine Latent Dynamics for Probabilistic Time Series Forecasting

Probabilistic time series forecasting seeks to quantify the uncertainty of future observations. While recent works introduce latent variables to alleviate the spurious dependencies caused by hidden confounders, thereby reducing overly wide confidence intervals, simply incorporating latent factors is