Definition
Bayesian structural time series (BSTS) models represent time series data as a sum of interpretable components such as trend, seasonality, and regression effects, while accounting for uncertainty through Bayesian inference. By placing priors on these components, BSTS allows for robust forecasting and causal impact estimation. This method is widely used in econometrics and marketing analytics to understand the effect of interventions on time-dependent outcomes, providing credible intervals for predictions rather than point estimates.
Summary
A statistical modeling approach that uses Bayesian inference to decompose time series into interpretable components.
Key Concepts
- State space models
- Causal inference
- Component decomposition
- Posterior distributions
Use Cases
- Marketing mix modeling
- Economic forecasting
- A/B testing with time series data