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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 │ -0.432945 │
│ 2020-01-01T01:00:00 │ -0.526391 │
│ 2020-01-01T02:00:00 │ -0.352362 │
│ 2020-01-01T03:00:00 │ -0.582685 │
│ 2020-01-01T04:00:00 │ -0.886383 │
│ 2020-01-01T05:00:00 │  -1.03134 │
│ 2020-01-01T06:00:00 │  -1.11099 │
│ 2020-01-01T07:00:00 │  -2.45525 │
│          ⋮          │     ⋮     │
│ 2020-01-07T17:00:00 │   4.08147 │
│ 2020-01-07T18:00:00 │    4.8562 │
│ 2020-01-07T19:00:00 │   3.54056 │
│ 2020-01-07T20:00:00 │   3.02163 │
│ 2020-01-07T21:00:00 │   4.65922 │
│ 2020-01-07T22:00:00 │   3.16547 │
│ 2020-01-07T23:00:00 │   3.69453 │
└─────────────────────┴───────────┘
                   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 │ -0.432945 │
│ 2020-01-01T00:15:00 │ -0.432945 │
│ 2020-01-01T00:30:00 │ -0.432945 │
│ 2020-01-01T00:45:00 │ -0.432945 │
│ 2020-01-01T01:00:00 │ -0.526391 │
│ 2020-01-01T01:15:00 │ -0.526391 │
│ 2020-01-01T01:30:00 │ -0.526391 │
│ 2020-01-01T01:45:00 │ -0.526391 │
│          ⋮          │     ⋮     │
│ 2020-01-07T21:30:00 │   4.65922 │
│ 2020-01-07T21:45:00 │   4.65922 │
│ 2020-01-07T22:00:00 │   3.16547 │
│ 2020-01-07T22:15:00 │   3.16547 │
│ 2020-01-07T22:30:00 │   3.16547 │
│ 2020-01-07T22:45:00 │   3.16547 │
│ 2020-01-07T23:00:00 │   3.69453 │
└─────────────────────┴───────────┘
                   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 │ -0.432945 │
│ 2020-01-01T00:15:00 │ -0.432945 │
│ 2020-01-01T00:30:00 │ -0.432945 │
│ 2020-01-01T00:45:00 │ -0.432945 │
│ 2020-01-01T01:00:00 │ -0.526391 │
│ 2020-01-01T01:15:00 │ -0.526391 │
│ 2020-01-01T01:30:00 │ -0.526391 │
│ 2020-01-01T01:45:00 │ -0.526391 │
│          ⋮          │     ⋮     │
│ 2020-01-01T22:30:00 │  -7.12602 │
│ 2020-01-01T22:45:00 │  -7.12602 │
│ 2020-01-01T23:00:00 │  -6.56802 │
│ 2020-01-01T23:15:00 │  -6.56802 │
│ 2020-01-01T23:30:00 │  -6.56802 │
│ 2020-01-01T23:45:00 │  -6.56802 │
│ 2020-01-02T00:00:00 │  -7.05261 │
└─────────────────────┴───────────┘
                    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 │ -0.432945 │
│ 2020-01-01T00:15:00 │ -0.432945 │
│ 2020-01-01T00:30:00 │ -0.432945 │
│ 2020-01-01T00:45:00 │ -0.432945 │
│ 2020-01-01T01:00:00 │ -0.526391 │
│ 2020-01-01T01:15:00 │ -0.526391 │
│ 2020-01-01T01:30:00 │ -0.526391 │
│ 2020-01-01T01:45:00 │ -0.526391 │
│          ⋮          │     ⋮     │
│ 2020-01-02T18:00:00 │  -12.0847 │
│ 2020-01-02T19:00:00 │  -14.6871 │
│ 2020-01-02T20:00:00 │  -14.2924 │
│ 2020-01-02T21:00:00 │  -14.1991 │
│ 2020-01-02T22:00:00 │   -14.909 │
│ 2020-01-02T23:00:00 │   -12.916 │
│ 2020-01-03T00:00:00 │  -13.2467 │
└─────────────────────┴───────────┘
                   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 │ -0.432945 │
│ 2020-01-01T00:15:00 │ -0.456307 │
│ 2020-01-01T00:30:00 │ -0.479668 │
│ 2020-01-01T00:45:00 │ -0.503029 │
│ 2020-01-01T01:00:00 │ -0.526391 │
│ 2020-01-01T01:15:00 │ -0.482883 │
│ 2020-01-01T01:30:00 │ -0.439376 │
│ 2020-01-01T01:45:00 │ -0.395869 │
│          ⋮          │     ⋮     │
│ 2020-01-07T21:30:00 │   3.91235 │
│ 2020-01-07T21:45:00 │   3.53891 │
│ 2020-01-07T22:00:00 │   3.16547 │
│ 2020-01-07T22:15:00 │   3.29773 │
│ 2020-01-07T22:30:00 │      3.43 │
│ 2020-01-07T22:45:00 │   3.56226 │
│ 2020-01-07T23:00:00 │   3.69453 │
└─────────────────────┴───────────┘
                   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 │  -0.635351 │
│ 2020-01-01T06:00:00 │   -3.12904 │
│ 2020-01-01T12:00:00 │   -6.03467 │
│ 2020-01-01T18:00:00 │    -6.3446 │
│ 2020-01-02T00:00:00 │   -8.23039 │
│ 2020-01-02T06:00:00 │   -9.29793 │
│ 2020-01-02T12:00:00 │   -11.6639 │
│ 2020-01-02T18:00:00 │   -13.8481 │
│          ⋮          │     ⋮      │
│ 2020-01-06T06:00:00 │   -1.20156 │
│ 2020-01-06T12:00:00 │   -2.55823 │
│ 2020-01-06T18:00:00 │   -3.76509 │
│ 2020-01-07T00:00:00 │  -0.522863 │
│ 2020-01-07T06:00:00 │ -0.0158669 │
│ 2020-01-07T12:00:00 │    1.77737 │
│ 2020-01-07T18:00:00 │    3.82293 │
└─────────────────────┴────────────┘
                     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 │ -7.12602 │
│ 2020-01-01T22:45:00 │ -7.12602 │
│ 2020-01-01T23:00:00 │ -6.56802 │
│ 2020-01-01T23:15:00 │ -6.56802 │
│ 2020-01-01T23:30:00 │ -6.56802 │
│ 2020-01-01T23:45:00 │ -6.56802 │
│ 2020-01-02T00:00:00 │ -7.05261 │
└─────────────────────┴──────────┘
                  178 rows omitted

Interpolation Methods

Available interpolation methods: Linear, Previous, Next, Nearest.

Aggregation Methods

Available aggregation methods: Mean, Min, Max, Count, Sum, Median, First, Last.

Extrapolation Methods

Available extrapolation methods: FillConstant, NearestExtrapolate, MissingExtrapolate, NaNExtrapolate.