Author = "Paulito P. Palmes"
Extracting the Date features in a Date,Value table follows
similar workflow with the [value preprocessing](@ref valueprep)
of the previous section. The main difference
is we are only interested on the
date corresponding to the last column of the values generated
by the Matrifier. This last column contains the values before
the prediction happens and the dates corresponding to these
values carry significant information based on recency compared
to the other dates.
Let us start by creating a Date,Value dataframe similar to the previous section.
using TSML
lower = DateTime(2017,1,1)
upper = DateTime(2018,1,31)
dat=lower:Dates.Day(1):upper |> collect
vals = rand(length(dat))
x = DataFrame(Date=dat,Value=vals)
nothing #hide
first(x,5)
Let us create an instance of Dateifier passing the size of row,
stride, and steps ahead to predict:
mtr = Dateifier(Dict(:ahead=>24,:size=>24,:stride=>5))
res = fit_transform!(mtr,x)
nothing #hide
first(res,5)
The model transform! output extracts automatically several date features
such as year, month, day, hour, week, day of the week,
day of quarter, quarter of year.
You can then combine the outputs in both the Matrifier and Datefier
as input features to a machine learning model. Below is an example of the
workflow where the code extracts the Date and Value features combining them
to form a matrix of features as input to a machine learning model.
commonargs = Dict(:ahead=>3,:size=>5,:stride=>2)
dtr = Dateifier(commonargs)
mtr = Matrifier(commonargs)
lower = DateTime(2017,1,1)
upper = DateTime(2018,1,31)
dat=lower:Dates.Day(1):upper |> collect
vals = rand(length(dat))
X = DataFrame(Date=dat,Value=vals)
valuematrix = fit_transform!(mtr,X)
datematrix = fit_transform!(dtr,X)
mlfeatures = hcat(datematrix,valuematrix)
nothing #hide
first(mlfeatures,5)
Another way is to use the symbolic pipeline to describe the transformation and concatenation in just one line of expression.
ppl = dtr + mtr
features = fit_transform!(ppl,X)
nothing #hide
first(features,5)