Simple auto_arima modelΒΆ

This is a simple example of how we can fit an ARIMA model in several lines without knowing anything about our data or optimal hyper parameters.


../_images/sphx_glr_example_simple_fit_001.png

Out:

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(1,0,1)[12] intercept   : AIC=2915.641, Time=0.30 sec
 ARIMA(0,1,0)(0,0,0)[12] intercept   : AIC=3049.597, Time=0.01 sec
 ARIMA(1,1,0)(1,0,0)[12] intercept   : AIC=2954.973, Time=0.12 sec
 ARIMA(0,1,1)(0,0,1)[12] intercept   : AIC=2947.014, Time=0.11 sec
 ARIMA(0,1,0)(0,0,0)[12]             : AIC=3047.612, Time=0.01 sec
 ARIMA(2,1,2)(0,0,1)[12] intercept   : AIC=2938.457, Time=0.20 sec
 ARIMA(2,1,2)(1,0,0)[12] intercept   : AIC=2915.636, Time=0.21 sec
 ARIMA(2,1,2)(0,0,0)[12] intercept   : AIC=2984.606, Time=0.09 sec
 ARIMA(2,1,2)(2,0,0)[12] intercept   : AIC=2915.168, Time=1.56 sec
 ARIMA(2,1,2)(2,0,1)[12] intercept   : AIC=2917.256, Time=2.41 sec
 ARIMA(1,1,2)(2,0,0)[12] intercept   : AIC=2924.379, Time=1.90 sec
 ARIMA(2,1,1)(2,0,0)[12] intercept   : AIC=2911.007, Time=1.59 sec
 ARIMA(2,1,1)(1,0,0)[12] intercept   : AIC=2911.505, Time=0.24 sec
 ARIMA(2,1,1)(2,0,1)[12] intercept   : AIC=2912.933, Time=1.18 sec
 ARIMA(2,1,1)(1,0,1)[12] intercept   : AIC=2911.904, Time=0.28 sec
 ARIMA(1,1,1)(2,0,0)[12] intercept   : AIC=2919.168, Time=0.72 sec
 ARIMA(2,1,0)(2,0,0)[12] intercept   : AIC=2927.636, Time=1.81 sec
 ARIMA(3,1,1)(2,0,0)[12] intercept   : AIC=2912.748, Time=2.50 sec
 ARIMA(1,1,0)(2,0,0)[12] intercept   : AIC=2954.725, Time=1.51 sec
 ARIMA(3,1,0)(2,0,0)[12] intercept   : AIC=2914.169, Time=2.29 sec
 ARIMA(3,1,2)(2,0,0)[12] intercept   : AIC=2915.130, Time=1.78 sec
 ARIMA(2,1,1)(2,0,0)[12]             : AIC=2908.085, Time=1.83 sec
 ARIMA(2,1,1)(1,0,0)[12]             : AIC=2908.791, Time=0.33 sec
 ARIMA(2,1,1)(2,0,1)[12]             : AIC=2909.997, Time=1.67 sec
 ARIMA(2,1,1)(1,0,1)[12]             : AIC=2907.980, Time=0.31 sec
 ARIMA(2,1,1)(0,0,1)[12]             : AIC=2932.875, Time=0.18 sec
 ARIMA(2,1,1)(1,0,2)[12]             : AIC=2909.946, Time=2.21 sec
 ARIMA(2,1,1)(0,0,0)[12]             : AIC=2979.838, Time=0.10 sec
 ARIMA(2,1,1)(0,0,2)[12]             : AIC=2920.616, Time=1.77 sec
 ARIMA(2,1,1)(2,0,2)[12]             : AIC=2911.929, Time=1.80 sec
 ARIMA(1,1,1)(1,0,1)[12]             : AIC=2915.102, Time=0.32 sec
 ARIMA(2,1,0)(1,0,1)[12]             : AIC=2925.280, Time=0.21 sec
 ARIMA(3,1,1)(1,0,1)[12]             : AIC=2910.007, Time=0.31 sec
 ARIMA(2,1,2)(1,0,1)[12]             : AIC=2913.030, Time=0.31 sec
 ARIMA(1,1,0)(1,0,1)[12]             : AIC=2952.472, Time=0.21 sec
 ARIMA(1,1,2)(1,0,1)[12]             : AIC=2922.341, Time=0.38 sec
 ARIMA(3,1,0)(1,0,1)[12]             : AIC=2912.111, Time=0.29 sec
 ARIMA(3,1,2)(1,0,1)[12]             : AIC=2913.230, Time=0.46 sec

Best model:  ARIMA(2,1,1)(1,0,1)[12]
Total fit time: 33.510 seconds

print(__doc__)

# Author: Taylor Smith <taylor.smith@alkaline-ml.com>

import pmdarima as pm
from pmdarima import model_selection
import numpy as np
from matplotlib import pyplot as plt

# #############################################################################
# Load the data and split it into separate pieces
data = pm.datasets.load_wineind()
train, test = model_selection.train_test_split(data, train_size=150)

# Fit a simple auto_arima model
arima = pm.auto_arima(train, error_action='ignore', trace=True,
                      suppress_warnings=True, maxiter=10,
                      seasonal=True, m=12)

# #############################################################################
# Plot actual test vs. forecasts:
x = np.arange(test.shape[0])
plt.scatter(x, test, marker='x')
plt.plot(x, arima.predict(n_periods=test.shape[0]))
plt.title('Actual test samples vs. forecasts')
plt.show()

Total running time of the script: ( 0 minutes 33.530 seconds)

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