autocorrelationdata visualization
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?
In R acf starts with lag 0, that is the correlation of a value with itself. pacf starts at lag 1.
Just a peculiarity of her R implementation. You can use the Acf function of the package forecast which does not show the lag 0 if that bothers you.
You ask .... what to do ...
I say I took your 121 daily values into AUTOBOX whose promary objective is to assess predictability from a sequence of observations for an interesting series to forecast possibly using ( not in this case ) user suggsted predictors . 
Your series is a discrete series insofar as only a partictular set of values can be observed (71,72,73,74,,,)
AUTOBOX looks for predictability using prior values (ARIMA) and in this case daily effects and possible changes in daily effects.
The equation ( with identified features ) is here 
, The suggestion is that the only identified feature was a change in day6 at period 83 (of 121) suggesting that week 1-11 was different from week 12-17 .
This suggests forecasts here 
Overall the Actual/Fit and Forecast graph is here 
The confidence intervals around the forecast are asymmetrical and include the possibility of future anomalies.
Best Answer
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.