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Time Series Analysis Skill · brycewang-stanford | DecimalAI

scienceby brycewang-stanford

Time Series Analysis Skill

brycewang-stanford/time-series

Source brycewang-stanford/Auto-Empirical-Research-Skills ↗License NOASSERTION

Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

SkillScore100Maintained

Automatic

Data Analyst pack

Last verified 8/7/2026

What it changes

Passed 22 of 22 test cases with the skill, and 20 of 22 without it.

Without skill91%

With skill100%

Checks it passed that the model alone missed

Recommends combining the ADF test with the KPSS test.
Includes both Dickey-Fuller and KPSS tests when evaluating stationarity.

2 fixed20 passed either way0 still failing0 broke

Pass-rate lift: +9 pp
Tokens: +105%
Agent turns: 0%
Response time: -9%

Model gemini-3.6-flashMeasured 8/7/2026v122 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-time-series/SKILL.md https://app.decimal.ai/s/brycewang-stanford-time-series/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add brycewang-stanford-time-series, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/brycewang-stanford-time-series/ and pins it in .decimal/skills.lock

decimalai skills add brycewang-stanford-time-series

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-time-series/SKILL.md https://app.decimal.ai/s/brycewang-stanford-time-series/SKILL.md

Raw file: /s/brycewang-stanford-time-series/SKILL.md ↗ · machine-readable summary at /s/brycewang-stanford-time-series.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 2

Prompt

I'm analyzing daily asset returns in R and need to build a univariate time series model. Can you walk me through the full workflow to identify appropriate ARMA/ARIMA orders using information criteria, test the model residuals for white noise, test for autoregressive conditional heteroskedasticity, and fit a GARCH model if volatility clustering is detected? Please provide a clean R script along with a summary of the diagnostic workflow.

What was checked, without and with the skill
Checked	Without skill	With skill
Includes both Dickey-Fuller and KPSS tests when evaluating stationarity.	Missed	Passed
Uses Ljung-Box test for checking residual autocorrelation.	Passed	Passed

case-01 · gemini-3.6-flash · 8/7/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md254 lines2 files

Time Series Analysis Skill

This skill provides guidance for univariate and multivariate time series analysis in empirical economics. It covers stationarity testing, ARIMA modeling, VAR/VECM systems, cointegration, and Granger causality.

Analysis Workflow

Step 1: Exploratory Inspection

Plot the series (level, first difference, log)
Examine ACF and PACF plots to identify dependence structure
Check for obvious trends, seasonality, structural breaks

Step 2: Stationarity Testing

Always test for unit roots before modeling. Preferred approach:

ADF test (H₀: unit root present)
KPSS test (H₀: series is stationary)
If ADF rejects AND KPSS fails to reject → stationary (I(0))
If both suggest non-stationary → take first difference, retest

ADF vs KPSS Conflict Resolution: When ADF fails to reject (suggests unit root) but KPSS also fails to reject (suggests stationary), the tests disagree. Recommended approach: (a) check for structural breaks — a break can make a stationary series look like it has a unit root; (b) use Zivot-Andrews test to allow for one structural break; (c) examine the series visually and consider economic theory. When in doubt, err on the side of differencing to avoid spurious regressions.

Step 3: Model Selection

Univariate: ARIMA(p,d,q) based on AIC/BIC and ACF/PACF
Multivariate, no cointegration: VAR in differences
Multivariate, with cointegration: VECM (VAR in error-correction form)

Step 4: Estimation and Diagnostics

Check residual white noise (Ljung-Box test)
Test for parameter stability (CUSUM)
Impulse response functions for structural interpretation

Step 5: Forecasting (if applicable)

Report RMSE and MAE on holdout sample
State forecast horizon and confidence intervals

Quick Code Templates

Stationarity Tests

# Python

Track record

Benchmark

22 / 22

test cases passed, vs 20 / 22 without

Adoption

Users: 3
Times used: 46

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

brycewang-stanford / Auto-Empirical-Research-Skills

skills/67-econfin-workflow-toolkit/time-series/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/2/2026Version v1Bundled 1 file

Other skills for the same job

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brycewang-stanford/time-series-guideApply ARIMA, VAR, cointegration, and time series econometric methodsClose match+17 pp pass-rate lift

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Time Series Analysis Skill

Analysis Workflow

Step 1: Exploratory Inspection

Step 2: Stationarity Testing

Step 3: Model Selection

Step 4: Estimation and Diagnostics

Step 5: Forecasting (if applicable)

Quick Code Templates

Stationarity Tests

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills