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)
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')