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  0.573884
   2 │ 2014-01-01T00:05:00  0.844533
   3 │ 2014-01-01T00:10:00  0.581939
   4 │ 2014-01-01T00:15:00  0.48463
   5 │ 2014-01-01T00:20:00  0.668158
   6 │ 2014-01-01T00:25:00  0.885289
   7 │ 2014-01-01T00:30:00  0.881523
   8 │ 2014-01-01T00:35:00  0.851777
   9 │ 2014-01-01T00:40:00  0.880195
  10 │ 2014-01-01T00:45:00  0.0383288

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  0.573884
   2 │ 2014-01-01T00:30:00  0.881523
   3 │ 2014-01-01T01:00:00  0.678097
   4 │ 2014-01-01T01:30:00  0.580916
   5 │ 2014-01-01T02:00:00  0.620842
julia> first(hourlyres,5)5×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64?
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.625049
   2 │ 2014-01-01T01:00:00  0.674655
   3 │ 2014-01-01T02:00:00  0.571333
   4 │ 2014-01-01T03:00:00  0.741202
   5 │ 2014-01-01T04:00:00  0.277196
julia> first(dailyres,5)5×2 DataFrame
 Row │ Date                 Value
     │ DateTime             Float64?
─────┼───────────────────────────────
   1 │ 2014-01-01T00:00:00  0.535325
   2 │ 2014-01-02T00:00:00  0.47691
   3 │ 2014-01-03T00:00:00  0.574036
   4 │ 2014-01-04T00:00:00  0.531759
   5 │ 2014-01-05T00:00:00  0.466941