def sindexf(decomp, h):
'''
Projects the seasonal component of a seasonal decomposition of a time series
forward by h time steps into the future.
Args:
decomp: an R seasonal decomposition from stl or decompose,
or a Pandas data frame containing a seasonal decomposition.
h: a forecast horizon
Returns:
an object that maps to am R time series containing the seasonal component
of decomp, projected naively forward h steps.
'''
if validate.is_Pandas_decomposition(decomp):
freq = len(decomp.index.levels[1])
seasonal = list(decomp.seasonal[-freq:])
it = itertools.cycle(seasonal)
data = []
for k in range(h):
data.append(it.next())
data = [0] + data
start = decomp.seasonal.last_valid_index()
series = converters.sequence_as_series(data, start=start, freq=freq)
return series[1:]
elif validate.is_R_decomposition(decomp):
return fc.sindexf(decomp, h)
else:
raise ValueError('seasadj requires a seasonal decomposition as input')
def test_sequence_as_series(self):
loil = list(self.oil_ts)
laus = list(self.aus_ts)
oil = converters.sequence_as_series(loil, start=1965)
aus = converters.sequence_as_series(laus, start=(1999, 1), freq=4)
self.assertEqual(oil.shape, (46,))
self.assertEqual(oil.index.nlevels, 1)
self.assertEqual(oil.index[0], 1965)
self.assertEqual(oil.index[-1], 2010)
self.assertAlmostEqual(oil[1965], 111.00, places=1)
self.assertAlmostEqual(oil[2010], 467.77, places=1)
self.assertEqual(aus.shape, (48, ))
self.assertEqual(aus.index.nlevels, 2)
self.assertEqual(aus.index[0], (1999, 1))
self.assertEqual(aus.index[-1], (2010, 4))
self.assertEqual(aus[2010].shape, (4,))
self.assertAlmostEqual(aus[(1999, 1)], 30.05, places=1)
self.assertAlmostEqual(aus[(2010, 4)], 47.91, places=1)
aus2 = converters.sequence_as_series(laus, start=1999, freq=4)
self.assertTrue(aus2.equals(aus))
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 test_sequence_as_series(self):
loil = list(self.oil_ts)
laus = list(self.aus_ts)
oil = converters.sequence_as_series(loil, start=1965)
aus = converters.sequence_as_series(laus, start=(1999, 1), freq=4)
self.assertEqual(oil.shape, (46, ))
self.assertEqual(oil.index.nlevels, 1)
self.assertEqual(oil.index[0], 1965)
self.assertEqual(oil.index[-1], 2010)
self.assertAlmostEqual(oil[1965], 111.00, places=1)
self.assertAlmostEqual(oil[2010], 467.77, places=1)
self.assertEqual(aus.shape, (48, ))
self.assertEqual(aus.index.nlevels, 2)
self.assertEqual(aus.index[0], (1999, 1))
self.assertEqual(aus.index[-1], (2010, 4))
self.assertEqual(aus[2010].shape, (4, ))
self.assertAlmostEqual(aus[(1999, 1)], 30.05, places=1)
self.assertAlmostEqual(aus[(2010, 4)], 47.91, places=1)
aus2 = converters.sequence_as_series(laus, start=1999, freq=4)
self.assertTrue(aus2.equals(aus))
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')
