I have a fairly large dataset with a few fields containing time-related data. This data comes in various shapes and sizes, but most of it can be parsed and rephrased in more appropriate formats for human-reading.
Formats range from
2010-01-01 to less obvious like
2008-52 (the second number is the number of editions a magazine had -- in this case, 1 for each week of the year).
Now I trying to transform it features for crunching. I first I thought it make sense to calculate the "age", i.e., the time difference between
now() and the timestamp I have in my data, and later normalize it. I know this works (computationally speaking), but I don't know if is the best approach or produce anything useful.
Is this approach useful? Is there a better way to handle "date" features?