time-series-guide
Apply ARIMA, VAR, cointegration, and time series econometric methods
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill time-series-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 import pandas as pd def test_stationarity(series: pd.Series, name: str = "Series") -> dict: """ Test for stationarity using ADF and KPSS tests. Args: series: Time series data name: Label for the series """ # Augmented Dickey-Fuller test # H0: Unit root exists (non-stationary) adf_result = adfuller(series.dropna(), autolag="AIC") # KPSS test # H0: Series is stationary kpss_result = kpss(series.dropna(), regression="c", nlags="auto") return { "series": name, "adf": { "statistic": adf_result[0], "p_value": adf_result[1], "lags_used": adf_result
- Stationarity and Unit Root Tests
- Why Stationarity Matters
- Testing for Stationarity
- Making a Series Stationary
- ARIMA Modeling
- Model Structure
- Model Selection and Fitting
- Vector Autoregression (VAR)
- Multivariate Time Series
- Granger Causality
- Cointegration Analysis
- Engle-Granger and Johansen Tests
- Diagnostic Checking
- Model Validation Checklist
What does the time-series-guide skill do?
Apply ARIMA, VAR, cointegration, and time series econometric methods
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill time-series-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.