How can I use a TimesNet trained model without re-training?

[Meekwest]

Greetings to all. I can't figure out how to use TimesNet for short-term forecasting after training and whether it is possible to make predictions without retraining. For example, I have historical data from 2018 to 2023, where I train a TimesNet model on a time series. These are indicators of the value of the company's shares in the form of
02.01.2024 07:00 297.34
02.01.2024 08:00 297.35
02.01.2024 09:00 297.36
02.01.2024 10:00 297.38
02.01.2024 11:00 297.42
02.01.2024 12:00 297.34
02.01.2024 13:00 297.34
02.01.2024 14:00 297.34
02.01.2024 15:00 297.31
02.01.2024 16:00 298.43

I set the following model parameters during training:
horizon = 2 # future 2 hours
model = TimesNet(h=horizon,
input_size=5*horizon,
max_steps=100,
top_k=3,
num_kernels=3,
batch_size=2,
windows_batch_size=32,
learning_rate=0.001,
futr_exog_list=[],
scaler_type='standard')

The last line in my training dataset is 02.01.2024 16:00 298.43
Immediately after learning, I try to predict on
02.01.2024 17:00
02.01.2024 18:00
TimesNet is doing well. But then I want to predict at 19:00 and 20:00, but already upload fresh data (because at the current time I already know what the actual amounts were at 17:00 and 18:00). I can't put fresh data into the model, I just can't predict on the trained model at 19:00 and 20:00 either, since she wants to see data for 17:00 and 18:00 in her trained dataset. So in order to predict a couple of hours ahead, given the actual data for the last 10 hours, do I need to retrain the model every time?