Apologies if this is not the right forum for asking this question. But I have tried other avenues but haven't gotten a satisfactory response. So, finally posting it here.
I've been exploring more advanced techniques for Multivariate Time Series Forecasting. Most of the resources that have been recommended like Forecasting: Principles and Practice, Time Series and its Applications, etc. are more about simple multivariate time series analysis involving one exogenous variable. These resources don't talk about the challenges involved in multivariate time series forecasting like forecasting input variables, feature selection, etc.
An example of the use cases that I am interested in is shared below
Main Objective: To predict the change in the direction and magnitude of the price of a cryptocurrency over the next day.
Exogenous Variables/Features: Current price of the currency, Amount of the currency sold in the last 24 hours, Change in the currency price in the last 24 hours, Change in the currency price in the last 1 hour, Number of new tweets in the last 24 hours that mention the currency
So, I was hoping that experienced Data Scientists can help me learn more advanced techniques to solve such problems. It would be great if you can recommend books and courses for a more detailed understanding.
Apologies for the long post. And thanks in advance.
TLDR: Resources to learn advanced forecasting techniques using multiple features.