Aggregation

DateValgator is a data type that supports operation for aggregation to minimize noise and lessen the occurrence of missing data. It expects to receive one argument which is the date-time interval for grouping values by taking their median. For example, hourly median as the basis of aggregation can be carried out by passing this argument: :dateinterval => Dates.Hour(1)

To illustrate DateValgator usage, let's start by generating an artificial data with sample frequencey every 5 minutes and print the first 10 rows.

using TSML

gdate = DateTime(2014,1,1):Dates.Minute(5):DateTime(2014,5,1)
gval = rand(length(gdate))
df = DataFrame(Date=gdate,Value=gval)

julia> first(df,10)10×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.630517
   2 │ 2014-01-01T00:05:00  0.20892
   3 │ 2014-01-01T00:10:00  0.761525
   4 │ 2014-01-01T00:15:00  0.898582
   5 │ 2014-01-01T00:20:00  0.411729
   6 │ 2014-01-01T00:25:00  0.654926
   7 │ 2014-01-01T00:30:00  0.845471
   8 │ 2014-01-01T00:35:00  0.631063
   9 │ 2014-01-01T00:40:00  0.114579
  10 │ 2014-01-01T00:45:00  0.16357

DateValgator

Let's apply the aggregator and try diffent groupings: hourly vs half hourly vs daily aggregates of the data.

using TSML

hourlyagg = DateValgator(Dict(:dateinterval => Dates.Hour(1)))
halfhourlyagg = DateValgator(Dict(:dateinterval => Dates.Minute(30)))
dailyagg = DateValgator(Dict(:dateinterval => Dates.Day(1)))

halfhourlyres = fit_transform!(halfhourlyagg,df)

hourlyres = fit_transform!(hourlyagg,df)

dailyres = fit_transform!(dailyagg,df)

The first 5 rows of half-hourly, hourly, and daily aggregates:

julia> first(halfhourlyres,5)5×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64?
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.630517
   2 │ 2014-01-01T00:30:00  0.845471
   3 │ 2014-01-01T01:00:00  0.214454
   4 │ 2014-01-01T01:30:00  0.237965
   5 │ 2014-01-01T02:00:00  0.355954
julia> first(hourlyres,5)5×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64?
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.642722
   2 │ 2014-01-01T01:00:00  0.683764
   3 │ 2014-01-01T02:00:00  0.38649
   4 │ 2014-01-01T03:00:00  0.580508
   5 │ 2014-01-01T04:00:00  0.540273
julia> first(dailyres,5)5×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64?
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.505389
   2 │ 2014-01-02T00:00:00  0.533083
   3 │ 2014-01-03T00:00:00  0.475736
   4 │ 2014-01-04T00:00:00  0.479515
   5 │ 2014-01-05T00:00:00  0.489647