  A regression-based BNN model is proposed to predict spatiotemporal quantities
like hourly rider demand with calibrated uncertainties. The main contributions
of this paper are (i) A feed-forward deterministic neural network (DetNN)
architecture that predicts cyclical time series data with sensitivity to
anomalous forecasting events; (ii) A Bayesian framework applying SVGD to train
large neural networks for such tasks, capable of producing time series
predictions as well as measures of uncertainty surrounding the predictions.
Experiments show that the proposed BNN reduces average estimation error by 10%
across 8 U.S. cities compared to a fine-tuned multilayer perceptron (MLP), and
4% better than the same network architecture trained without SVGD.
