Partial Autocorrelation Function
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A Partial Autocorrelation Function is a time series analysis function that gives the partial correlation of a time series with its own lagged values, controlling for the values of the time series at all shorter lags.
- AKA: PACF.
- See: Autoregressive Integrated Moving Average, Time Series Analysis, Partial Correlation, Autocorrelation Function, Autoregressive Model, Box–Jenkins.
References
2016
- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/partial_autocorrelation_function Retrieved:2016-8-10.
- In time series analysis, the partial autocorrelation function (PACF) gives the partial correlation of a time series with its own lagged values, controlling for the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags.
This function plays an important role in data analyses aimed at identifying the extent of the lag in an autoregressive model. The use of this function was introduced as part of the Box–Jenkins approach to time series modelling, where by plotting the partial autocorrelative functions one could determine the appropriate lags p in an AR (p) model or in an extended ARIMA (p,d,q) model.
- In time series analysis, the partial autocorrelation function (PACF) gives the partial correlation of a time series with its own lagged values, controlling for the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags.