Smoothing Data with Dates

General Usage
1

I have some data with an abscissa of DateTime type that I would like to smooth. I am familiar with SmoothingSplines so created my minimum non-working example with it.

I need to figure out how to put the DateTime into a type that SmoothingSplines understands. Once I have that I need to put it back into a DateTime type. Perhaps another smoothing package is more versatile?

Your input is appreciated.

using Dates
using Plots
using SmoothingSplines

dr = DateTime(2014, 01, 15):Day(1):DateTime(2014, 6, 1)  # DateTime range
length(dr)
 v = [x^1.35 + x*rand() for x in 1:length(dr)]  # ordinate data

 plot(dr,v)

 spl = fit(SmoothingSpline, dr, v, 5.0)
 spl = fit(SmoothingSpline, dump(dr), v, 5.0)

 vs = SmoothingSplines.predict(spl,dr)

 plot!(dr, v)
2

DateTime is a type that wraps the count of milliseconds from the Dates.jl epoch, which is a long time ago. To get a more useful value for your purpose, it makes sense to use counts relative to a more recent date. Your example suggests the information steps by Day rather than some smaller time unit. Assuming that is true,

using Dates
const Days2000 = Dates.value(Date(2000,1,1))
reltime(x::DateTime) = 
  Float64(Dates.value(Date(x)) - Days2000)

julia> const Epoch2000 = DateTime(2000,1,1)
2000-01-01T00:00:00

julia> const Date2000   = Date(Epoch2000)
2000-01-01

julia> const Days2000  = Dates.value(Date2000)
730120

julia> countdays(x::DateTime) = Dates.value(Date(x))  - Days2000
countdays (generic function with 1 method)

julia> reltime(x::DateTime) = Float64(countdays(x))
reltime (generic function with 1 method)

julia>

julia> datetime = DateTime(2014,1,1)
2014-01-01T00:00:00

julia> reltime(datetime)
5114.0

For smoothing splines, scaling the values (dividing by the value for the earliest date) is worthwhile and easy to invert.

3

Thank you for your help. Here is my now working MWE! It turns out that your hint of putting in a denominator for calculating the line fr was crucial at well.

using Dates
using Plots
using SmoothingSplines

dr = DateTime(2014, 01, 15):Day(1):DateTime(2014, 6, 1)  # DateTime range

length(dr)
 v = [x^1.35 + x*rand() for x in 1:length(dr)]  # ordinate data

 plot(dr,v)

 fr = Float64.(Dates.value.(dr) .- Dates.value(dr[1])) ./ Float64(Dates.value(DateTime(Day(1))))
 spl = fit(SmoothingSpline, fr, v, 100.0)
 
 vs = SmoothingSplines.predict(spl,fr)

 plot(dr, [v vs])
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