I'd like to compare the results of an LSTM model to an ARIMA model.
How can I create an ARIMA model in python that trains on the first 70% of data (~2700 observations), and then produces forecasts at 10-day intervals for the test data (~1200 observations) where the model sees all the previous data (expanding window) but does not get refit every 10 days? Thanks!
Right now my code refits the model every 10 days:
for t in range(0, len(test), 10): model = ARIMA(history, order=(5,1,0)) model_fit = model.fit(disp=0) output = model_fit.forecast(steps=10) yhat = output print(yhat) predictions.append(yhat.values()) obs = test[t] history.append(obs)