def test_append():
endog = dta['infl'].iloc[:100].values
mod = ARIMA(endog[:50], trend='c')
res = mod.fit()
res_e = res.append(endog[50:])
mod2 = ARIMA(endog)
res2 = mod2.filter(res_e.params)
assert_allclose(res2.llf, res_e.llf)
def test_append_with_exog_pandas():
# Pandas
endog = dta['infl'].iloc[:100]
exog = pd.Series(np.arange(len(endog)), index=endog.index)
mod = ARIMA(endog.iloc[:50], exog=exog.iloc[:50], trend='c')
res = mod.fit()
res_e = res.append(endog.iloc[50:], exog=exog.iloc[50:])
mod2 = ARIMA(endog, exog=exog, trend='c')
res2 = mod2.filter(res_e.params)
assert_allclose(res2.llf, res_e.llf)
def test_append_with_exog_and_trend():
# Numpy
endog = dta['infl'].iloc[:100].values
exog = np.arange(len(endog))**2
mod = ARIMA(endog[:50], exog=exog[:50], trend='ct')
res = mod.fit()
res_e = res.append(endog[50:], exog=exog[50:])
mod2 = ARIMA(endog, exog=exog, trend='ct')
res2 = mod2.filter(res_e.params)
assert_allclose(res2.llf, res_e.llf)
def test_forecast():
# Numpy
endog = dta['infl'].iloc[:100].values
mod = ARIMA(endog[:50], order=(1, 1, 0), trend='t')
res = mod.filter([0.2, 0.3, 1.0])
endog2 = endog.copy()
endog2[50:] = np.nan
mod2 = mod.clone(endog2)
res2 = mod2.filter(res.params)
assert_allclose(res.forecast(50), res2.fittedvalues[-50:])
def test_forecast_with_exog():
# Numpy
endog = dta['infl'].iloc[:100].values
exog = np.arange(len(endog))**2
mod = ARIMA(endog[:50], order=(1, 1, 0), exog=exog[:50], trend='t')
res = mod.filter([0.2, 0.05, 0.3, 1.0])
endog2 = endog.copy()
endog2[50:] = np.nan
mod2 = mod.clone(endog2, exog=exog)
print(mod.param_names)
print(mod2.param_names)
res2 = mod2.filter(res.params)
assert_allclose(res.forecast(50, exog=exog[50:]), res2.fittedvalues[-50:])
