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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

Autocorrelation plot

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 ?

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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.

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    $\begingroup$ Thank you. This helped ! $\endgroup$ – Senthil Feb 27 at 1:02

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