def test_one_step_ahead(setup_model):
model, params, results_R = setup_model
model2 = ETSModel(
pd.Series(model.endog),
seasonal_periods=model.seasonal_periods,
error=model.error,
trend=model.trend,
seasonal=model.seasonal,
damped_trend=model.damped_trend,
)
res = model2.smooth(params)
fcast1 = res.forecast(steps=1)
fcast2 = res.forecast(steps=2)
assert_allclose(fcast1.iloc[0], fcast2.iloc[0])
pred1 = res.get_prediction(start=model2.nobs,
end=model2.nobs,
simulate_repetitions=2)
pred2 = res.get_prediction(start=model2.nobs,
end=model2.nobs + 1,
simulate_repetitions=2)
df1 = pred1.summary_frame(alpha=0.05)
df2 = pred1.summary_frame(alpha=0.05)
assert_allclose(df1.iloc[0, 0], df2.iloc[0, 0])
# prior version [1]).
# Below you can see how to fit a simple exponential smoothing model using
# statsmodels's ETS implementation to this data. Additionally, the fit using
# `forecast` in R is shown as comparison.
model = ETSModel(oil)
fit = model.fit(maxiter=10000)
oil.plot(label="data")
fit.fittedvalues.plot(label="statsmodels fit")
plt.ylabel("Annual oil production in Saudi Arabia (Mt)")
# obtained from R
params_R = [
0.99989969, 0.11888177503085334, 0.80000197, 36.46466837, 34.72584983
]
yhat = model.smooth(params_R).fittedvalues
yhat.plot(label="R fit", linestyle="--")
plt.legend()
# By default the initial states are considered to be fitting parameters
# and are estimated by maximizing log-likelihood. It is possible to only use
# a heuristic for the initial values:
model_heuristic = ETSModel(oil, initialization_method="heuristic")
fit_heuristic = model_heuristic.fit()
oil.plot(label="data")
fit.fittedvalues.plot(label="estimated")
fit_heuristic.fittedvalues.plot(label="heuristic", linestyle="--")
plt.ylabel("Annual oil production in Saudi Arabia (Mt)")