ACF and PACF autocorrelation calculator
Measure how a series relates to earlier observations across successive lags.
ACF and PACF by lag
The dashed ±1.96/√n lines are approximate white-noise reference bounds rather than a complete model test.
Coefficients by lag
| Lag | ACF | PACF | Lower bound | Upper bound |
|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | ||||
How to use this page
- Paste an evenly ordered time series.
- Set a maximum lag below the number of observations.
- Compare ACF and PACF peaks with the reference bounds.
What this page does not do: The result describes the supplied series. Trend, seasonality, non-stationarity and a small sample can change the interpretation of ACF and PACF.
Further reading
ACF measures similarity between a series and a lagged copy. PACF uses the Durbin–Levinson recursion to show the relationship at one lag after removing shorter-lag linear effects.
A slowly decaying ACF may indicate trend or non-stationarity. Repeated peaks may correspond to seasonality, but require confirmation from process knowledge.
Frequently asked questions
How does PACF differ from ACF?
PACF at lag k removes the linear contribution of shorter lags, while ACF includes their indirect effects.
Does crossing a bound prove seasonality?
No. It is a signal for further diagnosis and should be assessed with trend, sampling frequency and process knowledge.
See also
Adjusted R-squared calculator
Convert R² to adjusted R² using the number of observations and predictors in a regression model.
Beta distribution calculator
Calculate beta density, CDF, upper tail and quantile together with its mean and variance.
Bonferroni correction calculator
Adjust alpha and p values with the Bonferroni method. Inspect the corrected threshold and decision for every t