def test_auto_arima(self):
fc = wrappers.auto_arima(self.oil)
self.assertAlmostEqual(fc.point_fc[2011], 475.7004, places=3)
self.assertAlmostEqual(fc.point_fc[2020], 547.0531, places=3)
self.assertAlmostEqual(fc.lower80[2011], 412.6839, places=3)
self.assertAlmostEqual(fc.upper95[2020], 851.8193, places=3)
fc = wrappers.auto_arima(self.aus)
self.assertAlmostEqual(fc.point_fc[(2011, 1)], 60.64208, places=3)
self.assertAlmostEqual(fc.point_fc[(2012, 4)], 51.55356, places=3)
self.assertAlmostEqual(fc.lower80[(2011, 1)], 57.57010, places=3)
self.assertAlmostEqual(fc.upper95[(2012, 4)], 57.52426, places=3)
self.assertRaises(ValueError, wrappers.auto_arima, self.oil ,
xreg=range(len(self.oil)))
self.assertRaises(ValueError, wrappers.auto_arima, self.oil, h=10,
newxreg=range(10))
'''
This example shows automatic fitting of an arima model with a linear
trend as a regressor. It is based on a post on Hyndsight, a blog by
R Forecast package author Rob J. Hyndman.
See: http://robjhyndman.com/hyndsight/piecewise-linear-trends/#more-3413
'''
# Not needed if the package is installed
import sys, os
sys.path.append(os.path.abspath('..'))
from rforecast import ts_io
from rforecast import wrappers
from rforecast import converters
from rforecast import plots
# This is how to import data that is installed in R.
stock = ts_io.read_ts('livestock', 'fpp')
n = len(stock)
fc = wrappers.auto_arima(stock, xreg=range(n), newxreg=range(n, n + 10))
print 'Australia livestock population 1961-2007'
print stock
plots.plot_ts(stock)
print '10-year forecast of livestock population'
print fc
plots.plot_forecast(fc, stock)
def test_auto_arima_seasonal(self):
fc_py = wrappers.auto_arima(self.aus_py)
model = self.fc.auto_arima(self.aus_r)
fc_r = self.fc.forecast(model)
self._check_points(fc_py, fc_r)
def test_auto_arima_seasonal(self): fc_py = wrappers.auto_arima(self.aus_py) model = self.fc.auto_arima(self.aus_r) fc_r = self.fc.forecast(model) self._check_points(fc_py, fc_r)
