I want to make a LSTM RNN for timeseries prediction, but some of my predictors are monthly and others are daily. Any advice / examples on how to set up this network?
The frequency of the predictions is monthly.
You can maybe get some inspiration from the ideas presented in this article which proposes a representation of the time series such that it deals with asynchronous sampling: you encode what is the source (the id of the time series) and the duration (time to the last value considering all time series) of the current value, and you end up with a single time series of values (as pictured in Figure 1 of the article that I attached below).
You could use a hierarchical structure. One LSTM can create an embedding vector for the sequence of daily predictors for each month. Then this embedding is fed into a second LSTM along with the monthly predictor variables.
Check the power demand experiments in this paper: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf. Different layers in a deep network can capture different time scales.