def test_autoreg_summary_corner():
data = sm.datasets.macrodata.load_pandas().data["cpi"].diff().dropna()
dates = period_range(start='1959Q1', periods=len(data), freq='Q')
data.index = dates
res = AutoReg(data, lags=4).fit()
summ = res.summary().as_text()
assert 'AutoReg(4)' in summ
assert 'cpi.L4' in summ
assert '03-31-1960' in summ
res = AutoReg(data, lags=0).fit()
summ = res.summary().as_text()
assert 'intercept' in summ
assert 'AutoReg(0)' in summ
def test_autoreg_summary_corner(old_names):
data = sm.datasets.macrodata.load_pandas().data["cpi"].diff().dropna()
dates = period_range(start="1959Q1", periods=len(data), freq="Q")
data.index = dates
res = AutoReg(data, lags=4, old_names=old_names).fit()
summ = res.summary().as_text()
assert "AutoReg(4)" in summ
assert "cpi.L4" in summ
assert "03-31-1960" in summ
res = AutoReg(data, lags=0, old_names=old_names).fit()
summ = res.summary().as_text()
if old_names:
assert "intercept" in summ
else:
assert "const" in summ
assert "AutoReg(0)" in summ
## AR(p) is simplest time-model, can nest in SARIMAX(p,d,q,s) with
## moving average MA(q), integration order I(d), seasonality S(s), exogenous X
from statsmodels.tsa.statespace.sarimax import SARIMAX
adf = alf(s, log=1, freq='Q').loc[19591201:20171231]
adf.index = pd.DatetimeIndex(adf.index.astype(str), freq='infer')
arima = SARIMAX(adf, order=(2, 1, 0), trend='c').fit()
fig = arima.plot_diagnostics(figsize=(10, 6))
plt.tight_layout(pad=2)
plt.savefig(os.path.join(imgdir, 'ar.jpg'))
plt.show()
arima.summary()
# Forecasting
## One-step ahead predictions
model = AutoReg(df_train, lags=lags, old_names=False).fit()
print(model.summary())
# Observations to predict are from the test split
from sklearn.metrics import mean_squared_error
all_dates = AutoReg(df, lags=lags, old_names=False)
df_pred = all_dates.predict(model.params,
start=df_train.index[-1]).shift(1).iloc[1:]
mse = mean_squared_error(df_test, df_pred)
var = np.mean(np.square(df_test - df_train.mean()))
print(f"Short-term Forecasts: rmse={np.sqrt(mse):.6f} r2={1-mse/var:.4f}")
fig, ax = plt.subplots(clear=True, num=1, figsize=(4, 6))
df_pred.plot(ax=ax, c='C0')
df_test.plot(ax=ax, c='C1')
ax.legend(['Predicted', 'Actual'])
ax.set_title(s + " (one-step forecasts)")
plt.tight_layout(pad=2)
