Bayesian structural time series

Bayesian structural time series (BSTS) model is a machine learning technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other. The model is designed to work with time series data.

The model has also promising application in the field of analytical marketing. In particular, it can be used in order to assess how much different marketing campaigns have contributed to the change in web search volumes, product sales, brand popularity and other relevant indicators (difference-in-differences model is a usual alternative approach in this case). "In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls."