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Retime

The retime function allows you to retime, i.e. change the timestamps of a TimeArray, similar to what Matlab's retime does.

using Plots, Dates, TimeSeries
gr()
timestamps = range(DateTime(2020, 1, 1), length = 7*24, step = Hour(1))
ta = TimeArray(timestamps, cumsum(randn(7*24)), [:a])
168×1 TimeArray{Float64, 1, DateTime, Vector{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │ 1.69092 │
│ 2020-01-01T01:00:00 │ 2.10902 │
│ 2020-01-01T02:00:00 │ 3.44779 │
│ 2020-01-01T03:00:00 │ 2.95738 │
│ 2020-01-01T04:00:00 │ 3.69434 │
│ 2020-01-01T05:00:00 │ 2.37074 │
│ 2020-01-01T06:00:00 │ 3.62711 │
│ 2020-01-01T07:00:00 │ 4.89666 │
│          ⋮          │    ⋮    │
│ 2020-01-07T17:00:00 │ 14.6916 │
│ 2020-01-07T18:00:00 │ 15.6407 │
│ 2020-01-07T19:00:00 │ 16.1868 │
│ 2020-01-07T20:00:00 │ 19.2493 │
│ 2020-01-07T21:00:00 │ 17.0703 │
│ 2020-01-07T22:00:00 │ 16.8454 │
│ 2020-01-07T23:00:00 │ 16.6527 │
└─────────────────────┴─────────┘
                 153 rows omitted

Using a new time step

retime(ta, Minute(15))
669×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │ 1.69092 │
│ 2020-01-01T00:15:00 │ 1.69092 │
│ 2020-01-01T00:30:00 │ 1.69092 │
│ 2020-01-01T00:45:00 │ 1.69092 │
│ 2020-01-01T01:00:00 │ 2.10902 │
│ 2020-01-01T01:15:00 │ 2.10902 │
│ 2020-01-01T01:30:00 │ 2.10902 │
│ 2020-01-01T01:45:00 │ 2.10902 │
│          ⋮          │    ⋮    │
│ 2020-01-07T21:30:00 │ 17.0703 │
│ 2020-01-07T21:45:00 │ 17.0703 │
│ 2020-01-07T22:00:00 │ 16.8454 │
│ 2020-01-07T22:15:00 │ 16.8454 │
│ 2020-01-07T22:30:00 │ 16.8454 │
│ 2020-01-07T22:45:00 │ 16.8454 │
│ 2020-01-07T23:00:00 │ 16.6527 │
└─────────────────────┴─────────┘
                 654 rows omitted

Using new timestep vector

new_timestamps = range(DateTime(2020, 1, 1), DateTime(2020, 1, 2), step = Minute(15))
retime(ta, new_timestamps)
97×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-02T00:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │ 1.69092 │
│ 2020-01-01T00:15:00 │ 1.69092 │
│ 2020-01-01T00:30:00 │ 1.69092 │
│ 2020-01-01T00:45:00 │ 1.69092 │
│ 2020-01-01T01:00:00 │ 2.10902 │
│ 2020-01-01T01:15:00 │ 2.10902 │
│ 2020-01-01T01:30:00 │ 2.10902 │
│ 2020-01-01T01:45:00 │ 2.10902 │
│          ⋮          │    ⋮    │
│ 2020-01-01T22:30:00 │ 7.08305 │
│ 2020-01-01T22:45:00 │ 7.08305 │
│ 2020-01-01T23:00:00 │ 9.23726 │
│ 2020-01-01T23:15:00 │ 9.23726 │
│ 2020-01-01T23:30:00 │ 9.23726 │
│ 2020-01-01T23:45:00 │ 9.23726 │
│ 2020-01-02T00:00:00 │ 8.69711 │
└─────────────────────┴─────────┘
                  82 rows omitted

Irregular timestamps

You can perform retime on irregularly spaced timestamps, both using a TimeArray with irregular timestamps or using a vector of irregular timestamps. Depending on the timestamps upsampling or downsampling is used. 

new_timestamps = vcat(
    range(DateTime(2020, 1, 1), DateTime(2020, 1, 2)-Minute(15), step = Minute(15)),
    range(DateTime(2020, 1, 2), DateTime(2020, 1, 3), step = Hour(1)),
)
retime(ta, new_timestamps)
121×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-03T00:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │ 1.69092 │
│ 2020-01-01T00:15:00 │ 1.69092 │
│ 2020-01-01T00:30:00 │ 1.69092 │
│ 2020-01-01T00:45:00 │ 1.69092 │
│ 2020-01-01T01:00:00 │ 2.10902 │
│ 2020-01-01T01:15:00 │ 2.10902 │
│ 2020-01-01T01:30:00 │ 2.10902 │
│ 2020-01-01T01:45:00 │ 2.10902 │
│          ⋮          │    ⋮    │
│ 2020-01-02T18:00:00 │ 18.1309 │
│ 2020-01-02T19:00:00 │  17.976 │
│ 2020-01-02T20:00:00 │ 16.0321 │
│ 2020-01-02T21:00:00 │ 14.4996 │
│ 2020-01-02T22:00:00 │ 14.3945 │
│ 2020-01-02T23:00:00 │ 15.0215 │
│ 2020-01-03T00:00:00 │ 14.1994 │
└─────────────────────┴─────────┘
                 106 rows omitted

Upsampling

Interpolation is done using the upsample argument. If no data is directly hit, the specified upsample method is used. Available upsample methods are:

  • Linear() or :linear
  • Nearest() or :nearest
  • Previous() or :previous
  • Next() or :next
ta_ = retime(ta, Minute(15), upsample=Linear())
669×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │ 1.69092 │
│ 2020-01-01T00:15:00 │ 1.79545 │
│ 2020-01-01T00:30:00 │ 1.89997 │
│ 2020-01-01T00:45:00 │  2.0045 │
│ 2020-01-01T01:00:00 │ 2.10902 │
│ 2020-01-01T01:15:00 │ 2.44371 │
│ 2020-01-01T01:30:00 │ 2.77841 │
│ 2020-01-01T01:45:00 │  3.1131 │
│          ⋮          │    ⋮    │
│ 2020-01-07T21:30:00 │ 16.9579 │
│ 2020-01-07T21:45:00 │ 16.9017 │
│ 2020-01-07T22:00:00 │ 16.8454 │
│ 2020-01-07T22:15:00 │ 16.7973 │
│ 2020-01-07T22:30:00 │ 16.7491 │
│ 2020-01-07T22:45:00 │ 16.7009 │
│ 2020-01-07T23:00:00 │ 16.6527 │
└─────────────────────┴─────────┘
                 654 rows omitted
plot(ta)
plot!(ta_)

Downsampling

Downsampling or aggregation is done using the downsample argument. This applies a function to each interval not including the right-edge of the interval. If no data is present in the interval the specified upsample method is used. Available downsample methods are:

  • Mean() or :mean
  • Min() or :min
  • Max() or :max
  • Count() or :count
  • Sum() or :sum
  • Median() or :median
  • First() or :first
  • Last() or :last
ta_ = retime(ta, Hour(6), downsample=Mean())
28×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T18:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2020-01-01T00:00:00 │  2.7117 │
│ 2020-01-01T06:00:00 │ 4.66567 │
│ 2020-01-01T12:00:00 │  7.9723 │
│ 2020-01-01T18:00:00 │ 8.29811 │
│ 2020-01-02T00:00:00 │ 9.96222 │
│ 2020-01-02T06:00:00 │ 13.8794 │
│ 2020-01-02T12:00:00 │ 16.9426 │
│ 2020-01-02T18:00:00 │ 16.0091 │
│          ⋮          │    ⋮    │
│ 2020-01-06T06:00:00 │   18.75 │
│ 2020-01-06T12:00:00 │ 21.5736 │
│ 2020-01-06T18:00:00 │ 17.2187 │
│ 2020-01-07T00:00:00 │ 17.6017 │
│ 2020-01-07T06:00:00 │ 18.3089 │
│ 2020-01-07T12:00:00 │  16.213 │
│ 2020-01-07T18:00:00 │ 16.9409 │
└─────────────────────┴─────────┘
                  13 rows omitted
plot(ta)
plot!(ta_)

Extrapolation

Extrapolation at the beginning and end of the time series is done using the extrapolate argument. Available extrapolate methods are:

  • FillConstant(value) or :fillconstant
  • NearestExtrapolate() or :nearest
  • MissingExtrapolate() or :missing
  • NaNExtrapolate() or :nan
new_timestamps = range(DateTime(2019, 12, 31), DateTime(2020, 1, 2), step = Minute(15))
ta_ = retime(ta, new_timestamps, extrapolate=MissingExtrapolate())
193×1 TimeArray{Union{Missing, Float64}, 2, DateTime, Matrix{Union{Missing, Float64}}} 2019-12-31T00:00:00 to 2020-01-02T00:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2019-12-31T00:00:00 │ missing │
│ 2019-12-31T00:15:00 │ missing │
│ 2019-12-31T00:30:00 │ missing │
│ 2019-12-31T00:45:00 │ missing │
│ 2019-12-31T01:00:00 │ missing │
│ 2019-12-31T01:15:00 │ missing │
│ 2019-12-31T01:30:00 │ missing │
│ 2019-12-31T01:45:00 │ missing │
│          ⋮          │    ⋮    │
│ 2020-01-01T22:30:00 │ 7.08305 │
│ 2020-01-01T22:45:00 │ 7.08305 │
│ 2020-01-01T23:00:00 │ 9.23726 │
│ 2020-01-01T23:15:00 │ 9.23726 │
│ 2020-01-01T23:30:00 │ 9.23726 │
│ 2020-01-01T23:45:00 │ 9.23726 │
│ 2020-01-02T00:00:00 │ 8.69711 │
└─────────────────────┴─────────┘
                 178 rows omitted