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time-series-social

Estimate stationarity, forecast dynamics, and test temporal relationships in social science time series.

Updated Aug 27, 2026
Search Tags:#time series#arima#var#structural breaks#social science#granger causality#intervention analysis
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill time-series-social
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: time-series-social
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/time-series-social
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill time-series-social

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, pandas, pmdarima, ruptures, statsmodels, and includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

Time series analysis helps you turn messy, time-indexed social data into reliable forecasts and effect estimates instead of misleading correlations.

Core Features & Use Cases

  • Stationarity & pre-model checks: Runs ADF and KPSS to decide whether to difference, transform, or model long-run relationships.
  • Core time-series models for social research: Builds ARIMA, VAR/Granger causality, cointegration tests, and VECM when long-run equilibrium matters.
  • Causal-adjacent policy/event evaluation over time: Supports structural break detection and intervention/event study-style analysis (step/pulse/ramp) to quantify temporal impacts.
  • Use Case: You have monthly public opinion, media sentiment, and a policy index over several years and want to (1) test temporal predictability (Granger), (2) model co-movements (VAR), and (3) detect regime shifts or policy effects around a known date.

Quick Start

Use this skill to analyze a dataset of social indicators over time and produce stationarity results, an ARIMA/VAR plan, and an intervention-impact assessment around a specified event date.

Frequently Asked Questions about time-series-social

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

FAQPage Schema
How do I run Granger causality tests on multiple time series in Python?▼

Granger causality tests on multiple time series require fitting a VAR model to check if past values of one variable predict another. This skill uses statsmodels to estimate temporal predictability and model co-movements for social indicators.

What is the best way to detect structural breaks in social science time series data?▼

Structural break detection identifies regime shifts in time-indexed data by locating significant change points. This skill uses the ruptures library to find regime shifts in social indicators around known policy dates or events.

How do I perform intervention analysis to measure policy effects over time?▼

Intervention analysis measures policy effects over time by fitting step, pulse, or ramp functions to a time series model. This skill supports event study-style analysis to quantify temporal impacts around a specified date.

Do I need to check stationarity before fitting ARIMA models?▼

You must check stationarity before fitting ARIMA models to decide whether to difference or transform your time series. This skill runs ADF and KPSS tests using statsmodels and pmdarima to validate pre-model assumptions.

Can I use cointegration and VECM for multiple related time series?▼

Cointegration and VECM apply to multiple related time series when modeling long-run equilibrium relationships. This skill tests cointegration and builds VECM using statsmodels for social indicators that move together over time.

What are the limitations of time series analysis for social science data?▼

Time series analysis for social science data requires a properly time-indexed dataset and relies on statistical assumptions about stationarity and temporal relationships. Misleading correlations can occur if structural breaks or non-stationarity are ignored.

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