Is there an R tutorial of using LSTM for multivariate time series forecasting? There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data sets, and the problem I am encountering is that some of the forecasting numbers are way off to a degree that are unreasonable. 

Therefore, I am wondering if there is an R tutorial of using LSTM for multivariate times series forecasting? I'd like to include variables like opening and closing price because I think that will "normalize" the forecasting values. I found a few tutorials in Python but I have limited experience with it. Maybe it's a time to pick up Python? Any advice or suggestions will be appreciated!
I like the Jena data example. In multivariate settings, you only need to generate lookbacks over all X.

https://blogs.rstudio.com/tensorflow/posts/2017-12-20-time-series-forecasting-with-recurrent-neural-networks/