errors = y.loc[start:] - y_hat
MAE = np.mean(np.absolute(errors))[0]
return MAE
TRAIN_OFFSET = 490
TEST_OFFSET = df_train.last_valid_index() + 1
from MLP import MLP
from Model import Model
from var_model import VAR as VAR
from FAVAR import FAVAR
results = {}
favar = FAVAR(factors_q)
favar = FAVAR(factors_q)
cols = ['CPI']
y_col = ['CPI']
# x_cols, y_col = get_cols(cols, 'GDP')
X_train, y_train = df_train[cols], df_train[y_col]
X_test, y_test = df_test[cols], df_test[y_col]
n_factors = 1
X_train = favar.addFactors(X_train, n_factors)
X_test = favar.addFactors(X_test, n_factors)
lags = 4
errors = y.loc[start:] - y_hat
MAE = np.mean(np.absolute(errors))[0]
return MAE
TRAIN_OFFSET = 490
TEST_OFFSET = df_train.last_valid_index() + 1
from MLP import MLP
from Model import Model
from var_model import VAR as VAR
from FAVAR import FAVAR
results = {}
favar = FAVAR(factors_q)
from MLP import MLP
cols = ['CPI']
y_col = ['CPI']
X_train, y_train = df_train[cols], df_train[y_col]
X_test, y_test = df_test[cols], df_test[y_col]
lags = 13
def gen_x(X, start):
X = VAR.gen_X(X, lags, start)
# X = X[:, 1:] # remove constant