Working on time series analysis in the economy indicators dataset, I probably uncovered the patterns of Logan Passengers and Logan International Flights variables. Till now in my analysis I have successfully done the Autocorrelation Function (ACF) and Partial Autocorrelation (PACF).
Autocorrelation Function (ACF):
- The correlation between a time series and its lagged values, or earlier data, is measured by the ACF.
- It shows the general dependence structure of the time series and computes the correlation coefficients for a variety of lags.
- Finding the order of a moving average (MA) process can be done with the help of the ACF. Compared to other kinds of processes, the autocorrelations in an MA process drop more slowly.
While working on ACF for building a pre-model, I got the plot for Logan Passengers and Logan International Flights. The number of lags to include in the plot is determined by the ‘lag’ parameter, which you can change to suit your needs. The width of the confidence interval surrounding the autocorrelation values is determined by the ‘alpha’ parameter.
The below graphs determined ACF:
Partial Autocorrelation Function (PACF):
- In contrast, the PACF eliminates the impact of the intermediate lags from the measurement of the correlation between a time series and its lagged values.
- By removing the influence of the intermediate lags, it aids in determining the direct relationship between observations at various lags.
- When determining the sequence of an autoregressive (AR) process, the PACF is especially helpful. For lags longer than the process order, the partial autocorrelations in an AR process go to zero.
Same as ACF, just need to update the library and I performed the Partial Autocorrelation Function (PACF) for the both the variables.
The below graphs shown the PACF:
