In this post I will be creating a call-volume forecast with my NYC EMS data using python’s tensorflow_probability library. In particular, I’ll employ that library’s sts method that provides the ability to create Bayesian structural time series (BSTS) forecasts. Structural Time Series Forecasting Unlike traditional time-series model forecasting, BSTS models can be used for prediction for multiple correlated time series while handling large variations in the short term. The Bayesian part, with respect to multivariate variables, assists the model to avoid over-fitting and identify correlations among the variables.