Interpretation of Autocorrelation plot

Question

I am trying to understand better how to read the autocorrelation plot here for a timeseries data.
I ran the following code and got the output as a chart show below.
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(df("y"))

Here y is the dependent variable

Should I derive the following conclusions

There are no significant autocorrelations.
The data is random.
Most of the correlations (except for 2 lags) fall within 95% confidence limits
This timeseries is not worth forecasting

Please help me if my understanding is right ?

WBM · Accepted Answer

To address your points:

There are no significant autocorrelations
The correlation is low (~0.25), but there are significant autocorrelations.

The data is random & most of the correlations (except for 2 lags) fall within 95% confidence limits
The confidence intervals are used to show which autocorrelations are significant. As you rightly observed, a couple peaks jump out of this region and this tells us that these few correlations are statistically significant, the rest is random. This post may be helpful here.

This timeseries is not worth forecasting
As per the previous point, there are a couple of statistically significant weak correlations in this dataset. But they are not strong, so a periodicity based forecasting model probably wouldn't be very accurate.

Interpretation of Autocorrelation plot

One Answer

Add your own answers!

Ask a Question