def setUp(self):
     self.oil_r = ts_io.read_ts('oil', 'fpp2', as_pandas=False)
     self.oil_py = converters.ts_as_series(self.oil_r)
     self.aus_r = ts_io.read_ts('austourists', 'fpp2', as_pandas=False)
     self.aus_py = converters.ts_as_series(self.aus_r)
     self.austa_r = ts_io.read_ts('austa', 'fpp2', as_pandas=False)
     self.austa_py = converters.ts_as_series(self.austa_r)
     self.fc = importr('forecast')
 def setUp(self):
   self.oil_r  = ts_io.read_ts('oil', 'fpp', as_pandas=False)
   self.oil_py = converters.ts_as_series(self.oil_r)
   self.aus_r = ts_io.read_ts('austourists', 'fpp', as_pandas=False)
   self.aus_py = converters.ts_as_series(self.aus_r)
   self.austa_r = ts_io.read_ts('austa', 'fpp', as_pandas=False)
   self.austa_py = converters.ts_as_series(self.austa_r)
   self.fc = importr('forecast')
 def setUp(self):
   self.oil = ts_io.read_ts('oil', 'fpp', False)
   self.aus = ts_io.read_ts('austourists', 'fpp', False)
   self.gold = ts_io.read_ts('gold', as_pandas=False)
   self.tsn = converters.ts([1, 2, NA, 4])
   self.tss = converters.ts([1, 2, 3, 1, 2, 3, 1, NA, 3], frequency=3)
   self.vss = [1,2,3,4] * 4
   self.vns = range(10)
   r = [ 0.00287731,  0.58436909,  0.37650672,  0.10024602,  0.46983146,
       0.36542408,  0.47136475,  0.79978803,  0.70349953,  0.69531808,
       0.54447409,  0.82227504,  0.99736304,  0.91404314,  0.42225177,
       0.14696605,  0.08098318,  0.11046747,  0.8412757 ,  0.73562921]
   self.rnd = converters.sequence_as_series(r, freq=4)
 def setUp(self):
     self.oil = ts_io.read_ts('oil', 'fpp', False)
     self.aus = ts_io.read_ts('austourists', 'fpp', False)
     self.gold = ts_io.read_ts('gold', as_pandas=False)
     self.tsn = converters.ts([1, 2, NA, 4])
     self.tss = converters.ts([1, 2, 3, 1, 2, 3, 1, NA, 3], frequency=3)
     self.vss = [1, 2, 3, 4] * 4
     self.vns = range(10)
     r = [
         0.00287731, 0.58436909, 0.37650672, 0.10024602, 0.46983146,
         0.36542408, 0.47136475, 0.79978803, 0.70349953, 0.69531808,
         0.54447409, 0.82227504, 0.99736304, 0.91404314, 0.42225177,
         0.14696605, 0.08098318, 0.11046747, 0.8412757, 0.73562921
     ]
     self.rnd = converters.sequence_as_series(r, freq=4)
     self.fc = importr('forecast')
    def setUp(self):
        importr('fpp2')
        #self.oil_ts = robjects.r('oil')
        #self.aus_ts = robjects.r('austourists')

        self.oil_ts = ts_io.read_ts('oil', 'fpp2', as_pandas=True)
        self.aus_ts = ts_io.read_ts('austourists', 'fpp2', as_pandas=True)
        self.fc_oil = wrappers.meanf(self.oil_ts)
        self.fc_aus = wrappers.ets(self.aus_ts)
        self.oil = ts_io.read_series('data/oil.csv')
        self.aus = ts_io.read_series('data/aus.csv')
        self.data = [
            0.74, 0.42, 0.22, 0.04, 0.17, 0.37, 0.53, 0.32, 0.82, 0.81, 0.11,
            0.79
        ]
        self.npdata = numpy.array(self.data)
示例#6
0
 def test_read_ts(self):
     oil = ts_io.read_ts('oil', 'fpp', as_pandas=True)
     self.assertEqual(len(oil), 46)
     self.assertListEqual(list(oil.index), range(1965, 2011))
     self.assertRaises(IOError, ts_io.read_ts, 'foo')
     self.assertRaises(IOError, ts_io.read_ts, 'oil', pkgname='foo')
'''
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_read_ts(self):
   oil = ts_io.read_ts('oil','fpp',as_pandas=True)
   self.assertEqual(len(oil), 46)
   self.assertListEqual(list(oil.index), range(1965, 2011))
   self.assertRaises(IOError, ts_io.read_ts, 'foo')
   self.assertRaises(IOError, ts_io.read_ts, 'oil', pkgname='foo')
 def setUp(self):
   self.oil = ts_io.read_series('data/oil.csv')
   self.aus = ts_io.read_series('data/aus.csv')
   self.austa = ts_io.read_ts('austa', 'fpp')