Reddit mentions of Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

It sounds like you're looking for time series material, but Applied Predictive Modeling may be of interest to you. For time series and R specifically, this text seems well-reviewed.

if you're doing this to help prepare to switch careers, look at industries and companies you might be interested in. Every vertical has different tech stack choices that are common. Medicine has a lot of SAS, pharmaceutical researchers I've met all use R, main industry and research at this point is mostly Python. Python gets you the most bang for your buck. If you need to step outside ML and throw together a back end DB, a REST API and a front end to glue the whole thing together or whatever, Python's just as useful there as it will be with ML. I don't use R, but from what I hear it's much less versatile. The Stats libraries for R are a lot more mature though apparently, so if you want to get into doing some more intense statistical stuff, I've heard Python is a little less friendly. I haven't run into any of those limitations, but I've been more playing around with RL and stuff, and doing less intense statistical analysis with rigorous confidence bounds or whatever.

For forecasting from historical data, you're looking at time series. Unfortunately I don't know a ton about time series modeling yet. It's much more complicated than a situation where you're assuming N iid draws from a stationary distribution (the 'typical' entry point for classification and such that you see in supervised machine learning).

Keeping in mind that I have no business giving you advice where to start because I haven't made the trek yet myself, I've heard good things about Time Series Analysis and Its Applications. It's a grad level stats book though, so I hope you aren't joking about your math background, haha. The examples in that book are all in R too, as a head's up.

For a slightly easier (but still standard) introduction to the topic, I've also heard Wei's Time Series Analysis is decent. If you look around for a good introduction to multivariable time series analysis though, I'm sure you could find a lot of resources and judge for yourself what would most fit your needs. If you did pick one of those two books to pound out, I suspect you'll have a radically better idea how to go the rest of the way and get into practical application. As you're getting into the theory (whatever resource you use), I'd highly recommend picking a few datasets you're interested in (Kaggle might be a good source, to go with whatever you care to get into for your own reasons) and as you go, try applying the various methods you're learning on those few different datasets to get some sense of how it works and why. Pro-tip: one or two of your go-to toy datasets should be generated yourself with some simple to understand function to help give a really easily understandable case to play with, where your intuition can still hold up. y(t) = sin(t) +kt + N(0,b) maybe, or some simple dynamic process of the form y^t+1 = f(y^t ).

But either way, make sure you're rolling up your sleeves and cracking your assumptions against actual data in code to make sure you get the idea. All theory and no practical makes Jack a dull boy.

Edit: if you want a more broad introduction without necessarily having the rigorous focus on time series forecasting, 'applied predictive modeling' and 'introduction to statistical learning' are both good big picture intros. The new hands on machine learning book is good too, but more narrow and less comprehensive. Elements of Statistical Learning is kind of the defacto standard reference text going over all the common algorithms from a mathematical perspective. If you have the mathematical maturity to tackle ELS, that'd be a great way to start to get a deep foundation in the theoretical ideas across ML as a whole, though obviously none of that is going to be time series specific.