Exemplo n.º 1
0
def test_oob_sarimax():
    xreg = rs.rand(wineind.shape[0], 2)
    fit = ARIMA(order=(1, 1, 1),
                seasonal_order=(0, 1, 1, 12),
                out_of_sample_size=15).fit(y=wineind, exogenous=xreg)

    fit_no_oob = ARIMA(order=(1, 1, 1),
                       seasonal_order=(0, 1, 1, 12),
                       out_of_sample_size=0,
                       suppress_warnings=True).fit(y=wineind[:-15],
                                                   exogenous=xreg[:-15, :])

    # now assert some of the same things here that we did in the former test
    oob = fit.oob()

    # compare scores:
    scoring = get_callable(fit_no_oob.scoring, VALID_SCORING)
    no_oob_preds = fit_no_oob.predict(n_periods=15, exogenous=xreg[-15:, :])
    assert np.allclose(oob, scoring(wineind[-15:], no_oob_preds), rtol=1e-2)

    # show params are still the same
    assert np.allclose(fit.params(), fit_no_oob.params(), rtol=1e-2)

    # show we can add the new samples and get the exact same forecasts
    xreg_test = rs.rand(5, 2)
    fit_no_oob.add_new_observations(wineind[-15:], xreg[-15:, :])
    assert np.allclose(fit.predict(5, xreg_test),
                       fit_no_oob.predict(5, xreg_test),
                       rtol=1e-2)

    # Show we can get a confidence interval out here
    preds, conf = fit.predict(5, xreg_test, return_conf_int=True)
    assert all(isinstance(a, np.ndarray) for a in (preds, conf))
Exemplo n.º 2
0
def test_with_intercept(order, seasonal):
    n_params = None
    for intercept in (False, True):
        modl = ARIMA(order=order,
                     seasonal_order=seasonal,
                     with_intercept=intercept).fit(lynx)

        if not intercept:  # first time
            n_params = modl.params().shape[0]
        else:
            # With an intercept, should be 1 more
            assert modl.params().shape[0] == n_params + 1
Exemplo n.º 3
0
def test_the_r_src():
    # this is the test the R code provides
    fit = ARIMA(order=(2, 0, 1), trend='c', suppress_warnings=True).fit(abc)

    # the R code's AIC = ~135
    assert abs(135 - fit.aic()) < 1.0

    # the R code's AICc = ~ 137
    assert abs(137 - fit.aicc()) < 1.0

    # the R code's BIC = ~145
    assert abs(145 - fit.bic()) < 1.0

    # R's coefficients:
    #     ar1      ar2     ma1    mean
    # -0.6515  -0.2449  0.8012  5.0370

    # note that statsmodels' mean is on the front, not the end.
    params = fit.params()
    assert_almost_equal(params, np.array([5.0370, -0.6515, -0.2449, 0.8012]),
                        decimal=2)

    # > fit = forecast::auto.arima(abc, max.p=5, max.d=5,
    #             max.q=5, max.order=100, stepwise=F)
    fit = auto_arima(abc, max_p=5, max_d=5, max_q=5, max_order=100,
                     seasonal=False, trend='c', suppress_warnings=True,
                     error_action='ignore')

    # this differs from the R fit with a slightly higher AIC...
    assert abs(137 - fit.aic()) < 1.0  # R's is 135.28
Exemplo n.º 4
0
def test_oob_sarimax():
    xreg = rs.rand(wineind.shape[0], 2)
    fit = ARIMA(order=(1, 1, 1),
                seasonal_order=(0, 1, 1, 12),
                maxiter=5,
                out_of_sample_size=15).fit(y=wineind, exogenous=xreg)

    fit_no_oob = ARIMA(order=(1, 1, 1),
                       seasonal_order=(0, 1, 1, 12),
                       out_of_sample_size=0,
                       maxiter=5,
                       suppress_warnings=True).fit(y=wineind[:-15],
                                                   exogenous=xreg[:-15, :])

    # now assert some of the same things here that we did in the former test
    oob = fit.oob()

    # compare scores:
    scoring = val.get_scoring_metric(fit_no_oob.scoring)
    no_oob_preds = fit_no_oob.predict(n_periods=15, exogenous=xreg[-15:, :])
    assert np.allclose(oob, scoring(wineind[-15:], no_oob_preds), rtol=1e-2)

    # show params are no longer the same
    assert not np.allclose(fit.params(), fit_no_oob.params(), rtol=1e-2)

    # show we can add the new samples and get the exact same forecasts
    xreg_test = rs.rand(5, 2)
    fit_no_oob.update(wineind[-15:], xreg[-15:, :])
    assert np.allclose(fit.predict(5, xreg_test),
                       fit_no_oob.predict(5, xreg_test),
                       rtol=1e-2)

    # And also the params should be close now after updating
    assert np.allclose(fit.params(), fit_no_oob.params())

    # Show we can get a confidence interval out here
    preds, conf = fit.predict(5, xreg_test, return_conf_int=True)
    assert all(isinstance(a, np.ndarray) for a in (preds, conf))
Exemplo n.º 5
0
def test_basic_arma():
    arma = ARIMA(order=(0, 0, 0), suppress_warnings=True)
    preds = arma.fit_predict(y)  # fit/predict for coverage

    # No OOB, so assert none
    assert arma.oob_preds_ is None

    # test some of the attrs
    assert_almost_equal(arma.aic(), 11.201, decimal=3)  # equivalent in R

    # intercept is param 0
    intercept = arma.params()[0]
    assert_almost_equal(intercept, 0.441, decimal=3)  # equivalent in R
    assert_almost_equal(arma.aicc(), 11.74676, decimal=5)
    assert_almost_equal(arma.bic(), 13.639060053303311, decimal=5)

    # get predictions
    expected_preds = np.array([0.44079876, 0.44079876, 0.44079876,
                               0.44079876, 0.44079876, 0.44079876,
                               0.44079876, 0.44079876, 0.44079876,
                               0.44079876])

    # generate predictions
    assert_array_almost_equal(preds, expected_preds)

    # Make sure we can get confidence intervals
    expected_intervals = np.array([
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139],
        [-0.10692387, 0.98852139]
    ])

    _, intervals = arma.predict(n_periods=10, return_conf_int=True,
                                alpha=0.05)
    assert_array_almost_equal(intervals, expected_intervals)
Exemplo n.º 6
0
def test_oob_for_issue_28():
    # Continuation of above: can we do one with an exogenous array, too?
    xreg = rs.rand(hr.shape[0], 4)
    arima = ARIMA(order=(2, 1, 2),
                  suppress_warnings=True,
                  out_of_sample_size=10).fit(y=hr, exogenous=xreg)

    oob = arima.oob()
    assert not np.isnan(oob)

    # Assert that the endog shapes match. First is equal to the original,
    # and the second is the differenced array, with original shape - d.
    assert np.allclose(arima.arima_res_.data.endog, hr, rtol=1e-2)
    assert arima.arima_res_.model.endog.shape[0] == hr.shape[0] - 1

    # Now assert the same for exog
    assert np.allclose(arima.arima_res_.data.exog, xreg, rtol=1e-2)
    assert arima.arima_res_.model.exog.shape[0] == xreg.shape[0] - 1

    # Compare the OOB score to an equivalent fit on data - 10 obs, but
    # without any OOB scoring, and we'll show that the OOB scoring in the
    # first IS in fact only applied to the first (train - n_out_of_bag)
    # samples
    arima_no_oob = ARIMA(order=(2, 1, 2),
                         suppress_warnings=True,
                         out_of_sample_size=0).fit(y=hr[:-10],
                                                   exogenous=xreg[:-10, :])

    scoring = get_callable(arima_no_oob.scoring, VALID_SCORING)
    preds = arima_no_oob.predict(n_periods=10, exogenous=xreg[-10:, :])
    assert np.allclose(oob, scoring(hr[-10:], preds), rtol=1e-2)

    # Show that the model parameters are exactly the same
    xreg_test = rs.rand(5, 4)
    assert np.allclose(arima.params(), arima_no_oob.params(), rtol=1e-2)

    # Now assert on the forecast differences.
    with_oob_forecasts = arima.predict(n_periods=5, exogenous=xreg_test)
    no_oob_forecasts = arima_no_oob.predict(n_periods=5, exogenous=xreg_test)

    assert_raises(AssertionError, assert_array_almost_equal,
                  with_oob_forecasts, no_oob_forecasts)

    # But after we update the no_oob model with the latest data, we should
    # be producing the same exact forecasts

    # First, show we'll fail if we try to add observations with no exogenous
    assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
                  None)

    # Also show we'll fail if we try to add mis-matched shapes of data
    assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
                  xreg_test)

    # Show we fail if we try to add observations with a different dim exog
    assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
                  xreg_test[:, :2])

    # Actually add them now, and compare the forecasts (should be the same)
    arima_no_oob.add_new_observations(hr[-10:], xreg[-10:, :])
    assert np.allclose(with_oob_forecasts,
                       arima_no_oob.predict(n_periods=5, exogenous=xreg_test),
                       rtol=1e-2)