No autocorrelation in time series I am trying to predict a time-series data set, using python. I have a timestamp and number of calls in a network for this particular timestamp. I have to predict number of calls in the future. Currently, I have 90 days of data and for every 20 minutes in a day i have an entry with number of calls. 

I resample the data so i plot the mean of the data for every 3 days and i get the following results:


I am not sure the trend graph is saying much. The data is going up and down so no obvious trend. However, there is seasonality. After plotting this, I checked for autocorrelation and this is where the weirdness is happening
I convert the created DataFrame to series and then plot it. 
This results in something weird, which shows just random values and no correlation

I do not know, if i am doing something wrong with my data, but if i have no autocorrelation and no stationarity, should i use Time Series analysis at all?
And in general can I make any predictions on this data, maybe with linear regression?
I am new to data science and i am doing this for my bachelor project, so i really need help. I have read a lot on the internet and maybe at this point i am pretty confused. Any help will be appreciated!
Regards
P.S
Here are some screen shots of acf and pacf plots with statsmodel library
First screenshot represents the data resampled to 3 days mean:

The acf and pacf for data resample to 1 day seems the same:

Here are the other plots as well for data resampled to 1 day


 A: Pandas isn't that well suited to analyze autocorrelation, that might be a source of problems in your data. The Statsmodels library in Python has better options. 
Try: 
statsmodels.tsa.stattools.acf
and
statsmodels.tsa.stattools.pacf
You're also looking at the mean of every 3 days of data, when it is likely that your data has 24 hour seasonality and 7 days seasonality, and both will get muddied by averaging over 3 days. 
A: 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.
