Bayesian structural time series

Bayesian Structural Time Series (BSTS) is a framework used for modeling and forecasting time series data that incorporates both structural components and Bayesian methods. The BSTS framework is particularly useful for analyzing data with complex patterns, such as trends, seasonality, and irregularities, while also allowing for the incorporation of various types of uncertainty. ### Key Components of Bayesian Structural Time Series: 1. **Structural Components**: - **Trend**: Captures long-term movements in the data.