I am new in modelling time series data using arima
I have a timeseries of similarity score calculated using Ademic Adar index. However when I use ARIMA it gives constant prediction to some data, what should I do?
This is what I have
Results=np.dstack([aa_matrix,aa_matrix_2,aa_matrix_3,aa_matrix_4,aa_matrix_5,aa_matrix_6])
Results=Results.reshape(-1)
size = int(len(Results) * 0.988597)
train, test = Results[0:size], Results[size:len(Results)]
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(train, order=(2,1,1))
model_fit = model.fit()
output = model_fit.forecast(steps=len(test))
Example output
[0.0026541988296397982, 0.002654198829639803, 0.002654198829639807, 0.0026541988296398104, 0.0026541988296398134, 0.002654198829639816, 0.002654198829639818, 0.00265419882963982, 0.0026541988296398212, 0.0026541988296398225, 0.0026541988296398234, 0.0026541988296398243, 0.002654198829639825, 0.0026541988296398256, 0.002654198829639826, 0.0026541988296398264, 0.002654198829639827, 0.0026541988296398273, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277, 0.0026541988296398277]
I have also read this Statsmodels ARIMA: Constant Value for Each Forecast Also I saw one comment says, try to add drift, but I don't know how to do it
Any help will be much appreciated Thank you in advance