advanced-math-trading/time-series-regimes

Identify regime shifts in financial time series for regime-aware trading models.

Updated Aug 27, 2026

Search Tags:#time-series #SDEs #regime-detection #GARCH #ARIMA

Ready to run

npx skills add https://github.com/keith-mvs/ordinis --skill advanced-math-trading-time-series-regimes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: advanced-math-trading/time-series-regimes
Source: https://github.com/keith-mvs/ordinis/tree/main/docs/knowledge-base/domains/skills/advanced-math-trading/time-series-regimes
Command: npx skills add https://github.com/keith-mvs/ordinis --skill advanced-math-trading-time-series-regimes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill covers regime-aware time-series modeling with ARIMA/GARCH, SDEs, and regime detection.

Core Features & Use Cases

Stationarity & ARIMA/GARCH: Modeling regimes and volatility
Regime Detection: Identify regime shifts and adapt strategies
SDEs & Jump Diffusions: Path modeling for simulations

Quick Start

Example: "Detect regimes in a price series and fit an ARIMA-GARCH model."

Frequently Asked Questions about advanced-math-trading/time-series-regimes

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

FAQPage Schema

How do I detect regime shifts in financial time series for trading?▼

Regime detection identifies structural breaks and shifts in market behavior by applying statistical tests and algorithms to historical price data. This Skill implements regime-detection workflows that segment time series into distinct regimes, enabling you to build trading models that adapt to changing market conditions.

What's the difference between ARIMA and GARCH modeling for time-series regimes?▼

ARIMA models temporal dependencies and trends in stationary series, while GARCH captures volatility clustering and regime-dependent volatility. This Skill combines both to fit regime-aware ARIMA-GARCH models that reflect how market volatility changes across detected regimes.

How do I test if a price series is stationary before modeling?▼

Stationarity testing applies statistical tests like unit-root tests to check whether a series has constant mean and variance over time. This Skill includes stationarity testing procedures required to validate whether your price data is suitable for ARIMA-GARCH estimation and regime modeling.

Can I use SDEs and jump diffusions to simulate price paths across regimes?▼

Yes. Stochastic differential equations model continuous price evolution with optional jump components, and regime-aware SDE discretization applies different parameters per regime. This Skill provides SDE discretization schemes that integrate regime detection to generate regime-conditioned Monte Carlo simulations.

What data do I need to apply regime detection and ARIMA-GARCH modeling?▼

You need historical price data—typically closing prices or returns—across multiple time horizons and assets. This Skill requires access to historical price series and applies stationarity tests, ARIMA-GARCH estimation, and regime-detection algorithms to transform raw price data into regime-aware trading signals.

How do I integrate regime detection into a diagnostic workflow?▼

Integration links regime identification with model diagnostics and backtesting by embedding detection algorithms into a multi-step pipeline. This Skill supports diagnostic workflows that test stationarity, fit regime-specific ARIMA-GARCH models, and validate regime transitions across multiple assets and time horizons.