It is a time series of approx 3500 values (10 years).
As you can see, there's high noise and high non stationarity, and we can observe seasonality. I want to fit both an ARIMA model and a recurrent neural network (RNN).
1) For the ARIMA model, should I differenciate to make the series stationary? If so, how do I get back to my original 'scale'? Or I should remove the trend and seasonality, work only with the residual part, and then add the original trend and seasonality at the end?
2) For the RNN model, would symbolic conversion reduce noise? Is there any other way to reduce the noise or some kind of data preprocessing that leads me into better results? Is there a way to smooth it for training and then 'de-smooth' it for prediction?
Any other ideas are more than welcome.
EDIT 1: You can find the dataset CSV file in the following link. In the image I only displayed one of the three columns (the values I deal with).