def test_ar_forecasting(self):
params = np.array([0.9])
forecasts = _ar_forecast(self.zero_mean, 5, 0, 0.0, params,
np.empty(0), np.empty(0))
expected = np.zeros((1000, 5))
expected[:, 0] = 0.9 * self.zero_mean.values
for i in range(1, 5):
expected[:, i] = 0.9 * expected[:, i - 1]
assert_allclose(forecasts, expected)
params = np.array([0.5, -0.3, 0.2])
forecasts = _ar_forecast(self.zero_mean, 5, 2, 0.0, params,
np.empty(0), np.empty(0))
expected = np.zeros((998, 8))
expected[:, 0] = self.zero_mean.iloc[0:-2]
expected[:, 1] = self.zero_mean.iloc[1:-1]
expected[:, 2] = self.zero_mean.iloc[2:]
for i in range(3, 8):
expected[:,
i] = (0.5 * expected[:, i - 1] -
0.3 * expected[:, i - 2] + 0.2 * expected[:, i - 3])
fill = np.empty((2, 5))
fill.fill(np.nan)
expected = np.concatenate((fill, expected[:, 3:]))
assert_allclose(forecasts, expected[2:])
def test_ar_forecasting(self):
params = np.array([0.9])
forecasts = _ar_forecast(self.zero_mean, 5, 0, 0.0, params)
expected = np.zeros((1000, 5))
expected[:, 0] = 0.9 * self.zero_mean.values
for i in range(1, 5):
expected[:, i] = 0.9 * expected[:, i - 1]
assert_allclose(forecasts, expected)
params = np.array([0.5, -0.3, 0.2])
forecasts = _ar_forecast(self.zero_mean, 5, 2, 0.0, params)
expected = np.zeros((998, 8))
expected[:, 0] = self.zero_mean.iloc[0:-2]
expected[:, 1] = self.zero_mean.iloc[1:-1]
expected[:, 2] = self.zero_mean.iloc[2:]
for i in range(3, 8):
expected[:, i] = 0.5 * expected[:, i - 1] - \
0.3 * expected[:, i - 2] + \
0.2 * expected[:, i - 3]
fill = np.empty((2, 5))
fill.fill(np.nan)
expected = np.concatenate((fill, expected[:, 3:]))
assert_allclose(forecasts, expected)