Partial Autocorrelation

Learn about partial autocorrelation, its relation to autocorrelation, and to plot a partial autocorrelogram.

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The partial autocorrelation coefficient

The partial autocorrelation coefficient $\lambda_j$ of a time series $y_t$ measures the linear relationship between observations that are $j$ periods apart, once the effect of all observations in between has been accounted for. In other words, $\lambda_j$ tells us how strong the impact that $y_{t-j}$ has on $y_t$ after subtracting the impact that observations $y_{t-j+1}, y_{t-j+2}, ..., y_{t-1}$ ...

Introduction to Time Series

The Basics of Time Series

Exploring Data

Analyze Time Series Data Using Markov Transition Fields

The Properties of Time Series

ARIMA Models

On Prediction

Choosing, Fitting, and Evaluating Models

Conclusion

Appendix

What have you learned?

Time Series Forecasting with Prophet in Python

Partial Autocorrelation

The partial autocorrelation coefficient