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module Y2016.M11.D28.Exercise where
import Control.Arrow (first)
import Data.Time
We're going to be looking at the messiness of the world (or: production data)
and how to model it for use here in code.
You have the below time series:
type Score = Int
timeSeries :: [(Day, Score)]
timeSeries =
zipWith (curry (first (read . ("2016-11-2" ++) . show))) [2..]
[11289979, 11422295, 11428000, 11564153, 11652241, 11841079]
-- so, eventually we'll look at curve-fitting (not today), but, first, let's
-- examine the messiness here. What are the gains, day-over-day?
gains :: Num a => [a] -> [a]
gains = undefined
-- The function gains takes a list of values [a,b,c,...] (assumed progressive,
-- or monotonically increasing here) and returns a list of the diffs between
-- those values: [b-a, c-b, ...]
-- (returned list is length - 1 of the input list)
-- How do you reformulate the timeSeries to make it a sensible input to gains?
-- What are the gains, day-over-day, of the timeSeries?
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