Does it handle complex seasonal patterns?

Yes, it provides implementations for additive seasonality with sum-to-zero constraints to ensure robust and identifiable seasonal decomposition.

Which Bayesian programming languages are supported?

This skill supports both Stan and JAGS, providing optimized code snippets and syntax for both probabilistic programming platforms.

Can I use this for financial volatility modeling?

Yes, it includes specialized templates for GARCH(1,1) models specifically designed to analyze and forecast volatility clustering in financial data.

How does this skill help with model validation?

It provides guidance on essential diagnostic checks, including stationarity verification, residual autocorrelation analysis (ACF/PACF), and one-step-ahead prediction comparisons.

Bayesian Time Series Modeling

Name: Bayesian Time Series Modeling
Author: choxos

bychoxos

0•

데이터 과학 및 ML

Implements robust Bayesian time series models using Stan and JAGS for advanced statistical forecasting and analysis.

This skill equips Claude with specialized knowledge to implement complex Bayesian time series models, ranging from basic autoregressive processes to sophisticated state-space and GARCH volatility models. It provides production-ready code snippets in Stan and JAGS, along with domain-specific guidance on stationarity constraints, seasonality handling, and diagnostic checking. This is an essential tool for data scientists and researchers performing probabilistic forecasting where rigorous uncertainty quantification and structural decomposition are required.

주요 기능

01Includes GARCH(1,1) configurations for modeling volatility clustering

02Offers additive seasonality templates for complex temporal patterns

03Implements state-space models including Local Level and Local Linear Trend

04Guides users through diagnostic checks like residual autocorrelation and stationarity

050 GitHub stars

06Provides Stan and JAGS implementations for AR, MA, and ARMA models

사용 사례

01Decomposing time series into trend and seasonal components using state-space frameworks

02Forecasting biological or econometric data with full uncertainty quantification

03Modeling risk and volatility in financial datasets using Bayesian GARCH models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent time-series-models

For use in Claude.ai and ChatGPT

주요 기능

01Includes GARCH(1,1) configurations for modeling volatility clustering

02Offers additive seasonality templates for complex temporal patterns

03Implements state-space models including Local Level and Local Linear Trend

04Guides users through diagnostic checks like residual autocorrelation and stationarity

050 GitHub stars

06Provides Stan and JAGS implementations for AR, MA, and ARMA models

사용 사례

01Decomposing time series into trend and seasonal components using state-space frameworks

02Forecasting biological or econometric data with full uncertainty quantification

03Modeling risk and volatility in financial datasets using Bayesian GARCH models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent time-series-models

For use in Claude.ai and ChatGPT