time series prediction using arima and non linear trend and too much residuals I am working on forecasting a financial index, i tried decomposing the time series using :

from matplotlib import pyplot
from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(dataset, model='multiplicative', freq=12)
result.plot()
pyplot.show()


And i got the following result: 



The results show that the time series is not stationary and it has a unit root (I used ADF and KPSS tests) and that the mean and std are constant in time!

I am wondering if i should use ARIMA or SARIMA since they are adapted to linear trend (my trend is not linear as shown in the image) or move to using LSTM, NN ... ?
Or even ARIMA or SARIMA are not adapted to this type of time series?
Long Short Term Memory (LSTM) is one option given that you have about 9 years of historical data.
You can take a model comparison approach where you split the data and see which algorithm is best at predicting the hold-out data.