4. Serializing your ARIMA models¶
After you’ve fit your model and you’re ready to start making predictions out
in your production environment, it’s time to save your ARIMA to disk.
Pmdarima models can be serialized with
joblib, just as with
most other python objects:
from pmdarima.arima import auto_arima from pmdarima.datasets import load_lynx import numpy as np # For serialization: import joblib import pickle # Load data and fit a model y = load_lynx() arima = auto_arima(y, seasonal=True) # Serialize with Pickle with open('arima.pkl', 'wb') as pkl: pickle.dump(arima, pkl) # You can still make predictions from the model at this point arima.predict(n_periods=5) # Now read it back and make a prediction with open('arima.pkl', 'rb') as pkl: pickle_preds = pickle.load(pkl).predict(n_periods=5) # Or maybe joblib tickles your fancy joblib.dump(arima, 'arima.pkl') joblib_preds = joblib.load('arima.pkl').predict(n_periods=5) # show they're the same np.allclose(pickle_preds, joblib_preds)