I'm currently tackling a problem of the following type:
Using machine learning to predict the output of factories given ten years of data (once a year every year). I have about 25 features including some qualitative (e.g. country, produce) and around 10,000 rows.
My problem is that I'm not sure how to tackle the time series whilst also including the different companies. If I only had one year, I'd treat the problem as a regression, but I'm not sure what to do with the time series. I was thinking of just adding them as features (e.g. "Employees in 2014", "Employees in 2013" etc.) but that seems like a bad idea as it removes the time element. How do people usually deal with this sort of thing? And what sort of regressor would people recommend I use for this kind of data?
There's also a problem that some factories are newer than others, so I only have a few years of data. Obviously, I could just use the factories that I have 10 years for, but I'd rather not.