ARIMA Model Predicting a straight line for my temperature data

I have a temperature dataset of 427 days(daily temperature data) I am training the ARIMA model for 360 days and trying to predict the rest of the 67 days data and comparing the results. While fitting the model in test data I am just getting a straight line as predictions, Am i doing something wrong? `

from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(train['max'],order=(1,1,2),)
results = model.fit()
results.summary()
start = len(train)
end = len(train) + len(test) -1
predictions= pd.DataFrame()
predictions['pred'] = results.predict(start=start, end=end, typ='levels').rename('ARIMA(1,1,1) Predictions')

Your ARIMA model uses the last two observations to make a prediction, that means:

• the prediction for t(361) is based on true values of t(360) and t(359).

• The prediction of t(362) is based on the already predicted t(361) and the true t(360).

• The prediction for t(363) is based on two predicted values, t(361) and t(360).

• The prediction for t(400) is based on predictions that are based on predictions that are based on predictions etc.

The prediction is based on previous predictions, and that means that forecasting errors will negatively impact new predictions. Imagine your prediction deviates only 1% for each time step, the forecasting error will become bigger and bigger the more time steps you try to predict. In such cases, the predictions often form a straight line at some point.

If you use an ARIMA(p, d, q) model, then you can forecast a maximum of q steps into the future. Predicting 67 steps into the future is a very far horizon, and ARIMA is most likely not able to do that. Instead, try to predict only the next single or few time steps.