A couple of months ago, I was talking to Anne Milley, director of analytical intelligence strategy at SAS. She was telling me about time-warping. That’s a method for assessing greater significance to events that happen in certain times.
The most common is to give more weight to the most recent events. The book I looked for yesterday is probably more a predictor of my interest tomorrow than one I searched for in 2004. But how much more relevant is it? Statisticans can study patterns across large populations and come up with time-warping formulas. I would imagine that they vary from sector to sector. A three-year-old search for hospice treatment probably has close to zero predictive power at this point. But if you were looking for Bob Dylan songs back then, you’re probably still interested.
- Advertisement -
This type of analysis is going to become ever more pervasive as we generate more time-stamped data with our smart phones. Of course, the trick then will be to warp for both time and place. The variations are endless.
Adrian Beltre
I would imagine that Nate Silver, the baseball and political statistician I interviewed last spring at South by SouthWest, has sophisticated time-warping …
A couple of months ago, I was talking to Anne Milley, director of analytical intelligence strategy at SAS. She was telling me about time-warping. That’s a method for assessing greater significance to events that happen in certain times.
The most common is to give more weight to the most recent events. The book I looked for yesterday is probably more a predictor of my interest tomorrow than one I searched for in 2004. But how much more relevant is it? Statisticans can study patterns across large populations and come up with time-warping formulas. I would imagine that they vary from sector to sector. A three-year-old search for hospice treatment probably has close to zero predictive power at this point. But if you were looking for Bob Dylan songs back then, you’re probably still interested.
- Advertisement -
This type of analysis is going to become ever more pervasive as we generate more time-stamped data with our smart phones. Of course, the trick then will be to warp for both time and place. The variations are endless.
Adrian Beltre
I would imagine that Nate Silver, the baseball and political
statistician I interviewed last spring at South by SouthWest, has
sophisticated time-warping models for baseball players. Since the Phillies are in the market for a third baseman, I’ve been thinking recently about Adrian Beltre, who had one great year at the hot corner for the Dodgers. As a
25-year-old, he hit 48 home runs in 2004 — but hasn’t hit more than 26
in a season since then. I would think that time-warping would almost
discount that one season as a near meaningless blip. Now that I think
about it, there’s a chance it’s not meaningless at all: After 2004, baseball
started testing much more vigorously for steroids.
That raises another challenge for statisticians: Drug warp.