[Python] Handling a priori on covariate variables for RNN

I am currently building my own dataloader for a deep learning project. The objective is to perform time series forecasting. As an exemple, let's assume I am forecasting weather, using the following dataframe:

X = pd.DataFrame(data={
'temperature': np.random.random((1, 10)).ravel(),
'pressure': np.random.random((1, 10)).ravel(),
'humidity': np.random.random((1, 10)).ravel(),
})

print(X.to_markdown())

temperature​

pressure​

humidity
0​

0.501873​

0.741631​

0.500776
1​

0.639229​

0.716319​

0.846043
2​

0.305061​

0.78736​

0.2809
3​

0.666592​

0.241905​

0.534717
4​

0.29799​

0.758383​

0.217077
5​

0.398248​

0.537553​

0.524409
6​

0.0699319​

0.706717​

0.74684
7​

0.707643​

0.821382​

0.29689
8​

0.620412​

0.788375​

0.512174
9​

0.0802374​

0.804594​

0.231062​

I want to predict the temperature at t+1 using the features at t-7, t-6, ..., t.
Now in addition to that, let's assume I have an a priori on the pressure data: I know it is relevant only for the past 2 days before the prediction (I only need the pressure at time t, t-1, t-2). Therefore, I do not want to add values of pressure prior to this because it will act as noise for the model. Additionally, my dataset is rather small which is why I want my data to be as useful as possible.

Since an RNN expects a dimension ( batch_size x n_timestep x feature_size ), how should I fill the values for the pressure during the time (t-7, ..., t-3).
Should I do a simple backfill where the pressure value at (t-7, ..., t-3) is equal to the pressure value at t-2 ?
Should I zero out the values at (t-7, ..., t-3) ?

Thanks in advance!

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