Recurrent Variational Information Bottleneck for Time Series Trend Prediction

Time series with large dispersion can be challenging for learning algorithms to predict intrinsic value, such as trend. To deal with this types of time series, we propose a novel method, namely the Recurrent Variational Information Bottleneck (RVIB). We first derive the objective function inspired by the Information Bottleneck principle. Then, the objective is integrated into a Gated Recurrent Unit architecture. Experiments on simulated time series with large and varied dispersion show that RVIB properly approximates their trends.