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A regression-based BNN model is proposed to predict spatiotemporal quantitieslike hourly rider demand with calibrated uncertainties. The main contributionsof this paper are (i) A feed-forward deterministic neural network (DetNN)architecture that predicts cyclical time series data with sensitivity toanomalous forecasting events; (ii) A Bayesian framework applying SVGD to trainlarge neural networks for such tasks, capable of producing time seriespredictions 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), and4% better than the same network architecture trained without SVGD.

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference