Making something from nothing

More often than not, I deal with datasets where I don’t have a multitude of features to choose from. It’s nothing like the Kaggle competitions where you usually have too many features, and need to figure out how to cull down the list. Sometimes I wonder what it would be like to work in an environment where you have people working on building a feature complete dataset, and the analysis team gets handed something where the data is as complete as possible.

Something I’ve been dealing with recent is a dataset where there are only 3 columns of data: the date, numeric_A, numeric_B. The plot of A vs B looks something like:

So essentially I’m given time series data, with data that looks like it could be linearly correlated. But how do we figure out more features, to even being to explore?

Seasonality

Of course this really depends on the data you’re dealing with, but sometimes you can extract a feature of what season the date is. This can simply be looking at the month and estimating what the season it is, or you can put in specific dates for each year and then bin them more accurately.

getSeason <- function(dates) {
    d <- month(dates)

    ifelse (d >= 12 | d < 3, "Summer",
      ifelse (d >= 9 & d < 12, "Spring",
        ifelse (d >= 3 & d < 6, "Fall", "Winter")))
}

Is there any other nature of the data that you can quantise? Basically the overview is to look at the year, month, and day components of the date and see if you can sort them into bins. Then accordingly you should have a couple more features that you can work with, and maybe combine to find some more.