Individual skill

time-series-guide

Apply ARIMA, VAR, cointegration, and time series econometric methods

skills/analysis/econometrics/time-series-guide/SKILL.md

Read pinned source

Source commit bf44b3cd617fa94c8a1b254c5d1987142ca3d631

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
312
Repository last updated
Jun 19, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Time Series Guide

A skill for applying time series econometric methods including ARIMA modeling, VAR systems, cointegration analysis, and unit root tests. Covers stationarity concepts, model selection, forecasting, and diagnostic checking for economic and financial data.

## Stationarity and Unit Root Tests

### Why Stationarity Matters

A time series is stationary when its statistical properties (mean, variance, autocorrelation) do not change over time. Most econometric methods require stationarity. Non-stationary series can produce spurious regressions.

### Testing for Stationarity

```python
from statsmodels.tsa.stattools import adfuller, kpss
Back to top