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07/31/2023, 4:44 PMMark
07/31/2023, 4:54 PMMark
07/31/2023, 4:54 PMPhil
07/31/2023, 4:55 PM# Horizon and quantiles
level = np.arange(0, 100, 2)
qs = [[50-lv/2, 50+lv/2] if lv!=0 else [50] for lv in level]
quantiles = np.sort(np.concatenate(qs)/100)
# HINT := BaseNetwork + Distribution + Reconciliation
nhits = NHITS(h=horizon,
input_size=24,
loss=GMM(n_components=10, quantiles=quantiles),
hist_exog_list=['month'],
max_steps=2000,
early_stop_patience_steps=10,
val_check_steps=50,
scaler_type='robust',
learning_rate=1e-3,
valid_loss=sCRPS(quantiles=quantiles))
model = HINT(h=horizon, S=S_df.values,
model=nhits, reconciliation='BottomUp')
Mark
07/31/2023, 4:58 PMPhil
07/31/2023, 4:58 PMMark
07/31/2023, 5:00 PMPhil
07/31/2023, 5:00 PMMark
07/31/2023, 5:04 PMMark
07/31/2023, 5:04 PMPhil
07/31/2023, 5:06 PMMark
07/31/2023, 5:06 PMMark
07/31/2023, 5:07 PMPhil
07/31/2023, 5:08 PMPhil
07/31/2023, 5:08 PMMark
07/31/2023, 5:08 PMPhil
07/31/2023, 5:08 PMMark
07/31/2023, 5:29 PMPhil
07/31/2023, 5:29 PMMark
07/31/2023, 5:29 PMMark
07/31/2023, 5:29 PMMark
07/31/2023, 5:30 PMPhil
07/31/2023, 5:30 PM