Most people seem to argue that lag plots and autocorrelation plots are useful for determining whether some univariate time series data is random or not.
I feel like I could accomplish this task by just plotting the time series normally. I don't see the point of a lag plot or a autocorrelation plot, plotting the raw time series seems to give a good enough intuition of the behaviour of the function.
Can someone enlighten me on the true usage of lag and autocorrelation plots?
Best Answer
There are two major advantages to making a lag autocorrelation plot
1) You get to see the number of significant lags of autocorrelation. This can not often be established just by looking at a time plot.
2) You get to estimate the lag autocorrelation, which indicates the strength of the correlation.
Aside from these two advantages, the point of statistical analysis is to summarize raw data with numerical quantities so as to abstract the noise from the signals. When data is tirelessly huge, naked eye analysis fails in addressing both the above points.
Below is an example of when naked eye analysis cannot help you differentiate between two time series plots, but an ACF plot helps.