How to remove non-stationarity? I want to make a model for predict the future values.
I have a time series like this one

So, I try to remove the trend (detrended) and with difference transform but nothing, I always have from the ADF test and KPSS test a values that indicate non-stationary dataset.
How can I do?
Sorry but the data was wrong, here is the correct data file: https://drive.google.com/open?id=1D0u9HwRnnkwH0bvaWGC-C59xpabua4u-
 A: First of all … you should ALWAYS model TIME SERIES i.e. bucketed data which is observed NOT what is accumulated UNLSS you wish to first bucket/accumulate transactional data to create a bucketed time series. The time series to be analyzed should never be an unneeded accumulation or an unneeded differencing.
The data you posted is here  , When you accumulated your data you injected non-stationarity (trend in this case) into your new series which you posted as a picture.
A useful model for the original data is obtained here containing the answer to your question.  . The evidence suggests that the non-stationarity in your observed data ( starting at 2016/4 ; 48 values ) is as follows:
1) there is a systematic seasonal pulse in December of each year (period 9) caused by an unspecified but latent exogenous deterministic effect possibly anthropogenic in nature.
2) there was a level shift DOWN at or about period 9 (2016/12)
3) there was 1 unusual activity DOWN at period 12 (2017/3)
4) there is significant positive correlation between observations 2 periods apart
I used AUTOBOX , which I have helped to develop, but essentially the analytical tools of Intervention Detection and arima model identification were simultaneously employed.
The residual plot is here  and the acf of the residuals suggesting sufficiency of the model is here 
The Actual/Fit and Forecast graph is here  providing integer forecasts and forecast intervals for the next 12 periods.
A: It depends if the non-stationarity is stocastic or deterministic. Most analysis focus on the former although solving for one won't solve the problem if you have the other. Differencing is the common way to deal with stocastic non-stationarity. Your data is obviously non-stationary. 
A: One common way to address non-stationarity is to take differences. Another (perhaps simpler) try you could do first is to take the log of your series. 
ADF test is your best friend. Also look at the ACF and PACF to get insights on the nature of the data before modeling time series.
