def test_basic_arima(): arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True) preds = arima.fit_predict(y) # fit/predict for coverage # test some of the attrs assert_almost_equal(arima.aic(), 11.201308403566909, decimal=5) assert_almost_equal(arima.aicc(), 11.74676, decimal=5) assert_almost_equal(arima.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 = arima.predict(n_periods=10, return_conf_int=True, alpha=0.05) assert_array_almost_equal(intervals, expected_intervals)
def test_basic_arima(): arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True) preds = arima.fit_predict(y) # fit/predict for coverage # test some of the attrs assert_almost_equal(arima.aic(), 11.201308403566909, decimal=5) assert_almost_equal(arima.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)