time-series-models

To build Bayesian time-series models in Stan or JAGS, you provide your sequential dataset and specify the model structure and priors to fit AR, MA, ARMA, state-space, or dynamic linear models for probabilistic forecasting.

A state-space model represents sequential data through latent states and observation equations. You need it for Bayesian time-series analysis when modeling dynamic processes like nowcasting or anomaly detection in domains such as epidemiology and environmental monitoring.

Yes, Bayesian time-series modeling supports anomaly detection and nowcasting across finance, engineering, epidemiology, and environmental science by fitting ARMA and state-space models to generate probabilistic forecasts with uncertainty estimates.

The best way to compare competing Bayesian time-series models is by using information criteria alongside MCMC diagnostics to evaluate fit and convergence, ensuring you accurately interpret posterior forecasts for your dynamic dataset.

Yes, you need to specify priors and model structures manually when fitting ARMA, state-space, and dynamic linear models in Stan or JAGS to satisfy Bayesian requirements and generate accurate posterior forecasts.

Bayesian time-series modeling provides probabilistic forecasts and explicit uncertainty estimates for sequential data, enabling better decision making in dynamic contexts where standard forecasting methods lack quantified confidence intervals.