Question about forecasting ahead to days outside training/validation set about pytorch-forecasting HOT 3 CLOSED

# select last 24 months from data (max_encoder_length is 24)
encoder_data = data[lambda x: x.time_idx > x.time_idx.max() - max_encoder_length]

# select last known data point and create decoder data from it by repeating it and incrementing the month
last_data = data[lambda x: x.time_idx == x.time_idx.max()]
decoder_data = pd.concat(
    [last_data.assign(date=lambda x: x.date + pd.offsets.MonthBegin(i)) for i in range(1, max_prediction_length + 1)],
    ignore_index=True,
)

# add time index consistent with "data"
decoder_data["time_idx"] = decoder_data["date"].dt.year * 12 + decoder_data["date"].dt.month
decoder_data["time_idx"] += encoder_data["time_idx"].max() + 1 - decoder_data["time_idx"].min()

# adjust additional time feature(s)
decoder_data["month"] = decoder_data.date.dt.month.astype(str).astype("category")  # categories have be strings

# combine encoder and decoder data
new_prediction_data = pd.concat([encoder_data, decoder_data], ignore_index=True)

new_raw_predictions, new_x = best_tft.predict(new_prediction_data, mode="raw",return_x=True)

for idx in range(10):  # plot 10 examples
    best_tft.plot_prediction(new_x, new_raw_predictions, idx=idx, show_future_observed=False);