摘要： You probably used random forest for regression and classification before, but time series forecasting? Hold up you’re going to say; time series data is special! And you’re right. When it comes to data that has a time dimension, applying machine learning (ML) methods becomes a little tricky.....
Benjamin Franklin said that only two things are certain in life: death and taxes. That explains why my colleagues at STATWORX were less than excited when they told me about their plans for the weekend a few weeks back: doing their income tax declaration. Man, I thought, that sucks, I’d rather spend this time outdoors. And then an idea was born.
What could taxes and the outdoors possibly have in common? Well, I asked myself: can we predict tax revenue using random forest? (wildly creative, I know). When dealing with tax revenue, we enter the realm of time series, ruled by fantastic beasts like ARIMA, VAR, STLM, and others. These are tried and proven methods, so why use random forests?
Well, you and I may both agree that random forest is one of the most awesome algorithms around: it’s simple, flexible, and powerful. So much so, that Wyner et al. (2015) call it the ‚off-the-shelf‘ tool for most data science applications. Long story short, it’s one of those algorithms that just works (if you want to know exactly how then check out this excellent post by my colleague Andre).
Random forest is a hammer, but is time series data a nail?
You probably used random forest for regression and classification before, but time series forecasting? Hold up you’re going to say; time series data is special! And you’re right. When it comes to data that has a time dimension, applying machine learning (ML) methods becomes a little tricky.
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