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?