Morning Poster Session: Towards Robust, Scalable and Interpretable Time Series Forecasting using Bayesian Vector Auto-Regression

We present a flexible, scalable, and interpretable framework for automated forecasting of multivariate time-series, building off of the Bayesian Vector Autoregression (BVAR) literature in macroeconometrics. Our algorithm allows for full posterior estimates of hundreds of interaction parameters, with minimal hand-tuning or hyperparameter specification required. The model can be easily extended to account for non-stationary breaks such as the COVID-19 pandemic. In experiments our model outperforms comparably-flexible time-series models at forecasting inflation.