Difference between Autocorrelation and Partial Autocorrelation
Autocorrelation (ACF) and Partial Autocorrelation (PACF) are both measures used in time series analysis to understand the relationships between observations at different time points.
Autocorrelation | Partial Autocorrelation |
|---|---|
Used for identifying the order of a moving average (MA) process. | Used for identifying the order of an autoregressive (AR) process. |
Represents the overall correlation structure of the time series. | Highlights the direct relationships between observations at specific lags. |
Autocorrelation measures the linear relationship between an observation and its previous observations at different lags. | Partial Autocorrelation measures the direct linear relationship between an observation and its previous observations at a specific lag, excluding the contributions from intermediate lags. |
Autocorrelation and Partial Autocorrelation
Autocorrelation and partial autocorrelation are statistical measures that help analyze the relationship between a time series and its lagged values. In R Programming Language, the acf() and pacf() functions can be used to compute and visualize autocorrelation and partial autocorrelation, respectively.