.. _sphx_glr_auto_examples_arima_example_persisting_a_model.py:
=========================
Persisting an ARIMA model
=========================
This example demonstrates how we can persist an ARIMA model to disk after
fitting it. It can then be loaded back up and used to generate forecasts.
.. raw:: html
.. rst-class:: sphx-glr-script-out
Out::
Predictions: array([21966.69715352, 25983.70920565, 30225.30978848, 35417.12496267,
13011.3618805 , 19639.41027742, 21506.58176159, 23674.33998321,
21685.76394174, 23669.77818948, 26955.35255979, 22755.2506162 ,
19808.16131764, 23578.7818493 , 27845.86719673, 32923.89426476,
10475.6877784 , 17024.74533391, 18831.64797186, 20929.546713 ,
18876.02414315, 20792.57512611, 24011.97546913, 19745.03906623,
16731.45362497, 20435.40470597, 24635.90938245, 29647.31030073,
7132.50096185, 13614.94373048, 15355.2376951 , 17386.52463115,
15266.3918605 , 17116.33182084, 20269.12156224, 15935.57434204,
12855.37819397, 16492.71851157, 20626.61245363, 25571.40262264,
2989.9825421 , 9405.81456517, 11079.49778624, 13044.1739777 ,
10857.430463 , 12640.75967901, 15726.93867622, 11326.78071176,
8179.97381947, 11750.70339282, 15817.98659065])
|
.. code-block:: python
print(__doc__)
# Author: Taylor Smith
import pmdarima as pm
from sklearn.externals import joblib # for persistence
import os
# #############################################################################
# Load the data and split it into separate pieces
y = pm.datasets.load_wineind()
train, test = y[:125], y[125:]
# Fit an ARIMA
arima = pm.ARIMA(order=(1, 1, 2), seasonal_order=(0, 1, 1, 12))
arima.fit(y)
# #############################################################################
# Persist a model and create predictions after re-loading it
pickle_tgt = "arima.pkl"
try:
# Pickle it
joblib.dump(arima, pickle_tgt, compress=3)
# Load the model up, create predictions
arima_loaded = joblib.load(pickle_tgt)
preds = arima_loaded.predict(n_periods=test.shape[0])
print("Predictions: %r" % preds)
finally:
# Remove the pickle file at the end of this example
try:
os.unlink(pickle_tgt)
except OSError:
pass
**Total running time of the script:** ( 0 minutes 5.362 seconds)
.. only :: html
.. container:: sphx-glr-footer
.. container:: sphx-glr-download
:download:`Download Python source code: example_persisting_a_model.py `
.. container:: sphx-glr-download
:download:`Download Jupyter notebook: example_persisting_a_model.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
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