I seemed to have solved the problem by myself, but I'm not really sure whether my approach is right or not. But I'll try to explain in details what I'm trying to achieve here.
On the dataset, there are 29 variables listed below (the names are taken directly from the file header):
date time, Appliances, lights, T1, RH_1, T2, RH_2, T3, RH_3, T4, RH_4, T5, RH_5, T6, RH_6, T7, RH_7, T8, RH_8, T9, RH_9, To, Pressure, RH_out, Wind speed, Visibility, Tdewpoint, rv1, rv2
I then used all of the variables as observations (x), meaning I set “Appliances” as first element of x (x_1), “lights” as the second element (x_2), “T1” as the third element (x_3), and so on. Here is a snippet of the form of the dataset. The vertical axis represents time.
I processed the dataset to follow the format of the tutorial (http://pyro.ai/examples/dmm.html), and the only changes I made to the model is to set the Emitter function into producing mean and variance of the Normal distribution of the dataset, since the dataset consists of real values.
What I'm trying to do here, is to train the model on the dataset, with all the variables intact, but then remove one of the variables during evaluation and make the model infer/predict the missing variable from the latent states produced by the guide.
To achieve this, I changed the guide to take all the variables minus the missing variable, and then reintroduce all the variables (including the missing one) to the model during emittance. I'm training the model right now, and the NLL seems to be decreasing. Don't know whether it'll work or not though.
If there are some details that are still unclear, I'll try to clarify them. Thanks for the help!