Bayesian structural time series is a statistical technique used to analyze time-varying data. It combines elements of Bayesian statistics with time series analysis to infer the underlying structure of the data and make predictions. The technique uses probabilistic models to capture the dynamics of the data and provides flexible and adaptable estimates, allowing for non-linear trends, seasonality, and outliers. It finds application in various fields, such as forecasting, finance, and environmental modeling, where understanding the underlying dynamics of time-series data is crucial.
