# > set.seed(123)
# > abc <- rnorm(50, 5, 1)
abc = np.array([
4.439524, 4.769823, 6.558708, 5.070508, 5.129288, 6.715065, 5.460916,
3.734939, 4.313147, 4.554338, 6.224082, 5.359814, 5.400771, 5.110683,
4.444159, 6.786913, 5.497850, 3.033383, 5.701356, 4.527209, 3.932176,
4.782025, 3.973996, 4.271109, 4.374961, 3.313307, 5.837787, 5.153373,
3.861863, 6.253815, 5.426464, 4.704929, 5.895126, 5.878133, 5.821581,
5.688640, 5.553918, 4.938088, 4.694037, 4.619529, 4.305293, 4.792083,
3.734604, 7.168956, 6.207962, 3.876891, 4.597115, 4.533345, 5.779965,
4.916631
])
wineind = load_wineind()
lynx = load_lynx()
def test_basic_arima():
arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True)
preds = arima.fit_predict(y) # fit/predict for coverage
# test some of the attrs
assert_almost_equal(arima.aic(), 11.201308403566909, decimal=5)
assert_almost_equal(arima.aicc(), 11.74676, decimal=5)
assert_almost_equal(arima.bic(), 13.639060053303311, decimal=5)
# get predictions
expected_preds = np.array([
0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876,
0.44079876, 0.44079876, 0.44079876, 0.44079876
.. raw:: html
<br/>
"""
print(__doc__)
# Author: Taylor Smith <*****@*****.**>
from pyramid.datasets import load_lynx
from pyramid.arima import auto_arima
import matplotlib.pyplot as plt
import numpy as np
# #############################################################################
# Load the data and split it into separate pieces
data = load_lynx()
train, test = data[:100], data[100:]
# #############################################################################
# Fit with some validation (cv) samples
arima = auto_arima(train,
start_p=1,
start_q=1,
d=0,
max_p=5,
max_q=5,
out_of_sample_size=10,
suppress_warnings=True,
stepwise=True,
error_action='ignore')
error_action='ignore', # don't want to know if an order does not work
suppress_warnings=True, # don't want convergence warnings
stepwise=True) # set to stepwise
stepwise_fit.summary()
from pyramid.arima import auto_arima
from pyramid.datasets import load_lynx
import numpy as np
# For serialization:
from sklearn.externals 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')