time-series-models

Fit Bayesian AR, MA, ARMA, and state-space models using Stan or JAGS.

9|1|Updated Jan 10, 2026

Search Tags:#forecasting #time-series #bayesian #state-space #stan #jags

Ready to run

npx skills add https://github.com/choxos/BiostatAgent --skill time-series-models

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: time-series-models
Source: https://github.com/choxos/BiostatAgent/tree/main/plugins/bayesian-modeling/skills/time-series-models
Command: npx skills add https://github.com/choxos/BiostatAgent --skill time-series-models

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bayesian time-series modeling provides probabilistic forecasts and uncertainty estimates for sequential data, enabling better decision making in dynamic contexts.

Core Features & Use Cases

AR, MA, ARMA, and state-space models implemented in Stan and JAGS for flexible forecasting.
Tools for model specification, diagnostics, and comparison of competing time-series structures.
Use cases include forecasting demand, anomaly detection, and nowcasting in finance, engineering, epidemiology, and environmental monitoring.

Quick Start

Provide your time-series data and run the included Stan or JAGS workflows to fit AR/MA/ARMA/state-space models.

Frequently Asked Questions about time-series-models

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build Bayesian time-series models in Stan or JAGS for forecasting?▼

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.

What is a state-space model and when do I need it for Bayesian time-series analysis?▼

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.

Can I use this approach for anomaly detection and nowcasting in finance and engineering?▼

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.

What's the best way to compare competing time-series models fitted with MCMC?▼

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.

Do I need to specify priors manually when fitting ARMA and dynamic linear models?▼

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.

Why use Bayesian time-series modeling instead of standard forecasting methods?▼

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.