def test_exog(self):
# check that trend and exog are equivalent for basics and varsim
data = self.res0.model.endog
res_lin_trend = VAR(data).fit(maxlags=2, trend="ct")
ex = np.arange(len(data))
res_lin_trend1 = VAR(data, exog=ex).fit(maxlags=2)
ex2 = np.arange(len(data))[:, None]**[0, 1]
res_lin_trend2 = VAR(data, exog=ex2).fit(maxlags=2, trend="n")
# TODO: intercept differs by 4e-3, others are < 1e-12
assert_allclose(res_lin_trend.params, res_lin_trend1.params, rtol=5e-3)
assert_allclose(res_lin_trend.params, res_lin_trend2.params, rtol=5e-3)
assert_allclose(res_lin_trend1.params,
res_lin_trend2.params,
rtol=1e-10)
y1 = res_lin_trend.simulate_var(seed=987128)
y2 = res_lin_trend1.simulate_var(seed=987128)
y3 = res_lin_trend2.simulate_var(seed=987128)
assert_allclose(y2.mean(0), y1.mean(0), rtol=1e-12)
assert_allclose(y3.mean(0), y1.mean(0), rtol=1e-12)
assert_allclose(y3.mean(0), y2.mean(0), rtol=1e-12)
h = 10
fc1 = res_lin_trend.forecast(res_lin_trend.endog[-2:], h)
exf = np.arange(len(data), len(data) + h)
fc2 = res_lin_trend1.forecast(res_lin_trend1.endog[-2:],
h,
exog_future=exf)
with pytest.raises(ValueError, match="exog_future only has"):
wrong_exf = np.arange(len(data), len(data) + h // 2)
res_lin_trend1.forecast(res_lin_trend1.endog[-2:],
h,
exog_future=wrong_exf)
exf2 = exf[:, None]**[0, 1]
fc3 = res_lin_trend2.forecast(res_lin_trend2.endog[-2:],
h,
exog_future=exf2)
assert_allclose(fc2, fc1, rtol=1e-12, atol=1e-12)
assert_allclose(fc3, fc1, rtol=1e-12, atol=1e-12)
assert_allclose(fc3, fc2, rtol=1e-12, atol=1e-12)
fci1 = res_lin_trend.forecast_interval(res_lin_trend.endog[-2:], h)
exf = np.arange(len(data), len(data) + h)
fci2 = res_lin_trend1.forecast_interval(res_lin_trend1.endog[-2:],
h,
exog_future=exf)
exf2 = exf[:, None]**[0, 1]
fci3 = res_lin_trend2.forecast_interval(res_lin_trend2.endog[-2:],
h,
exog_future=exf2)
assert_allclose(fci2, fci1, rtol=1e-12, atol=1e-12)
assert_allclose(fci3, fci1, rtol=1e-12, atol=1e-12)
assert_allclose(fci3, fci2, rtol=1e-12, atol=1e-12)
def generate_final_predictions(df_coords, lag_order=3, display=False):
'''
Uses the best lag_order (from testing_harness) to train the full model
and forecast mean coordinates for the years 2022 and 2023. Returns a DF
'''
model = VAR(endog=df_coords)
model = model.fit(lag_order)
forecast = model.forecast(model.y, steps=2)
df_forecast = pd.DataFrame(forecast,
columns=['future_latitude', 'future_longitude'])
df_forecast['year'] = [2022, 2023]
df_forecast = df_forecast[['year', 'future_latitude', 'future_longitude']]
if display:
print()
print('Final model information:')
print()
print(model.summary())
print()
print('Future hotspot forecasts:')
print()
print(df_forecast)
return df_forecast
df['Ibov_fut'] = df['Ibov_fut'] / df['Spot']
train_size = int(len(df) * 0.7)
test_size = len(df) - train_size
train, test = df[0:train_size], df[train_size:len(df)]
train2 = train.copy()
pred = pd.DataFrame()
model = VAR(train2).fit(maxlags=15, ic='aic')
lag_order = model.k_ar
for i in range(len(test)):
yhat = model.forecast(train2.values[-lag_order:], 1)
train2 = train2.append(test.iloc[i, :])
pred = pred.append(pd.DataFrame(yhat))
print(i, lag_order)
vol_pred = pred.iloc[:, -1]
vol_test = test.iloc[:, -1]
mean_squared_error(vol_test, vol_pred)
model = VAR(train).fit(maxlags=15, ic='aic')
lag_order = model.k_ar
yhat = model.forecast(train.values[-lag_order:], 1)
yhat
datas = df['Data']
if lenght > max_lenght:
max_lenght = lenght
next_line = np.array(
current_path_df.drop(
columns=['id_line', 'index_path']).iloc[lenght + 1])
#print(current_path_df)
array_route = np.array(
current_path_df.head(lenght).drop(
columns=['id_line', 'index_path']))
#print(array_route)
current_train_df = pd.DataFrame(
[create_training_path(current_path_df.head(lenght), size)])
prediction_1 = model_1.forecast(array_route, steps=1)[0]
prediction_2 = model_2.forecast(array_route, steps=1)[0]
prediction_3 = model_3.forecast(array_route, steps=1)[0]
prediction_4 = model_4.forecast(array_route, steps=1)[0]
# Define closest prediction
dist_1 = distance_between_literal(prediction_1[1], prediction_1[2],
next_line[1], next_line[2])
dist_2 = distance_between_literal(prediction_2[1], prediction_2[2],
next_line[1], next_line[2])
dist_3 = distance_between_literal(prediction_3[1], prediction_3[2],
next_line[1], next_line[2])
dist_4 = distance_between_literal(prediction_4[1], prediction_4[2],
next_line[1], next_line[2])
# Use time as distance too, centering using the value of the day
