--- title: Giacomini-White Test keywords: fastai sidebar: home_sidebar nb_path: "nbs/losses__gw_test.ipynb" ---
np.random.seed(1)
loss1 = np.random.randint(low=1, high=10, size=(10,1))
loss2 = np.random.randint(low=1, high=10, size=(10,1))
GW_CPA_test(loss1=loss1, loss2=loss2, tau=1, conditional=True)
np.random.seed(117)
# Observed values for 3 different days
y = np.random.randint(low=1, high=10, size=(100*24))
# Predicted values for 3 different days, from 5 different models
y_hat = np.random.randint(low=1, high=10, size=(100*24, 5))
model_names = ['Model1', 'Model2', 'Model3', 'Model4', 'Model5']
pvals = GW_test_pvals(y=y, y_hat=y_hat, horizon=24, tau=1,
conditional=True, alpha=0.05, verbose=False)
plot_GW_test_pvals(pvals=pvals, labels=model_names, title='GW_test')
# data = pd.read_csv('./results/ver.csv')
# model_names = ['AR1', 'ESRNN', 'NBEATS', 'ARX1', 'LEAR', 'DNN', 'NBEATS_X_G', 'NBEATS_X_I']
# pvals = GW_test_pvals(y=np.expand_dims(data['y'].to_numpy(), axis=0),
# y_hat=data.loc[:, model_names].to_numpy(),
# horizon=24,
# tau=1,
# conditional=True,
# alpha=0.05,
# verbose=False)
# plot_GW_test_pvals(pvals=pvals, labels=model_names, title='GW_test')