Autocorrelation and partial autocorrelation interpretation

Question

I am learning about ACF and PACF graphs. I am not sure I understand how to interpret the one I got for my data.

I have searched google for some ACF and PACF examples, and I found some samples of different processes, however, the one I am getting doesn't look similar to any. Does this means there is no seasonality, trend and other processes?

I have also created graphs for unstandardized residuals of my model, do not understand what those means? Does it somehow relate to white noise?

ACF & PACF of y and residuals

Yes. Sixteen. These are not real data, some hypotethical data for learning from some lectures. — renathy, May 22 '13 at 08:55
There's little reason to believe there's any autocorrelation structure there, but with 16 observations you have little power. — Glen_b, May 22 '13 at 09:26

Scortchi - Reinstate Monica · Accepted Answer · 2013-05-22T09:23:23.847

Neither the ACF nor the PACF are giving any reason to suppose an ARMA process, trend or seasonality: none of the correlations approach significance at conventional levels. Note that sixteen observations is very few to fit a time series model, so the only effects you might see would be very large ones.

The residuals of the process are the differences between the observations & the fitted values from your model. If your model's good they should be white noise—uncorrelated with zero mean. You don't say what model you fit; but the residuals look a little less like white noise than your original series, so it's probably not a good one.

Autocorrelation and partial autocorrelation interpretation

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