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 │ -2.50129 │
│ 2020-01-01T01:00:00 │ -3.21346 │
│ 2020-01-01T02:00:00 │ -3.38029 │
│ 2020-01-01T03:00:00 │ -4.78287 │
│ 2020-01-01T04:00:00 │ -5.17299 │
│ 2020-01-01T05:00:00 │ -5.39028 │
│ 2020-01-01T06:00:00 │ -4.99481 │
│ 2020-01-01T07:00:00 │ -5.09049 │
│          ⋮          │    ⋮     │
│ 2020-01-07T17:00:00 │ -6.83117 │
│ 2020-01-07T18:00:00 │ -7.33849 │
│ 2020-01-07T19:00:00 │ -8.10188 │
│ 2020-01-07T20:00:00 │ -7.07485 │
│ 2020-01-07T21:00:00 │ -7.41208 │
│ 2020-01-07T22:00:00 │  -7.6519 │
│ 2020-01-07T23:00:00 │ -6.27549 │
└─────────────────────┴──────────┘
                  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 │ -2.50129 │
│ 2020-01-01T00:15:00 │ -2.50129 │
│ 2020-01-01T00:30:00 │ -2.50129 │
│ 2020-01-01T00:45:00 │ -2.50129 │
│ 2020-01-01T01:00:00 │ -3.21346 │
│ 2020-01-01T01:15:00 │ -3.21346 │
│ 2020-01-01T01:30:00 │ -3.21346 │
│ 2020-01-01T01:45:00 │ -3.21346 │
│          ⋮          │    ⋮     │
│ 2020-01-07T21:30:00 │ -7.41208 │
│ 2020-01-07T21:45:00 │ -7.41208 │
│ 2020-01-07T22:00:00 │  -7.6519 │
│ 2020-01-07T22:15:00 │  -7.6519 │
│ 2020-01-07T22:30:00 │  -7.6519 │
│ 2020-01-07T22:45:00 │  -7.6519 │
│ 2020-01-07T23:00:00 │ -6.27549 │
└─────────────────────┴──────────┘
                  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 │ -2.50129 │
│ 2020-01-01T00:15:00 │ -2.50129 │
│ 2020-01-01T00:30:00 │ -2.50129 │
│ 2020-01-01T00:45:00 │ -2.50129 │
│ 2020-01-01T01:00:00 │ -3.21346 │
│ 2020-01-01T01:15:00 │ -3.21346 │
│ 2020-01-01T01:30:00 │ -3.21346 │
│ 2020-01-01T01:45:00 │ -3.21346 │
│          ⋮          │    ⋮     │
│ 2020-01-01T22:30:00 │ -9.84625 │
│ 2020-01-01T22:45:00 │ -9.84625 │
│ 2020-01-01T23:00:00 │ -9.51302 │
│ 2020-01-01T23:15:00 │ -9.51302 │
│ 2020-01-01T23:30:00 │ -9.51302 │
│ 2020-01-01T23:45:00 │ -9.51302 │
│ 2020-01-02T00:00:00 │ -9.57593 │
└─────────────────────┴──────────┘
                   82 rows omitted

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 │ -2.50129 │
│ 2020-01-01T00:15:00 │ -2.50129 │
│ 2020-01-01T00:30:00 │ -2.50129 │
│ 2020-01-01T00:45:00 │ -2.50129 │
│ 2020-01-01T01:00:00 │ -3.21346 │
│ 2020-01-01T01:15:00 │ -3.21346 │
│ 2020-01-01T01:30:00 │ -3.21346 │
│ 2020-01-01T01:45:00 │ -3.21346 │
│          ⋮          │    ⋮     │
│ 2020-01-02T18:00:00 │ -8.57448 │
│ 2020-01-02T19:00:00 │ -7.31647 │
│ 2020-01-02T20:00:00 │ -8.73729 │
│ 2020-01-02T21:00:00 │ -8.46124 │
│ 2020-01-02T22:00:00 │ -7.99706 │
│ 2020-01-02T23:00:00 │ -8.15694 │
│ 2020-01-03T00:00:00 │ -7.66989 │
└─────────────────────┴──────────┘
                  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 │ -2.50129 │
│ 2020-01-01T00:15:00 │ -2.67933 │
│ 2020-01-01T00:30:00 │ -2.85737 │
│ 2020-01-01T00:45:00 │ -3.03542 │
│ 2020-01-01T01:00:00 │ -3.21346 │
│ 2020-01-01T01:15:00 │ -3.25516 │
│ 2020-01-01T01:30:00 │ -3.29687 │
│ 2020-01-01T01:45:00 │ -3.33858 │
│          ⋮          │    ⋮     │
│ 2020-01-07T21:30:00 │ -7.53199 │
│ 2020-01-07T21:45:00 │ -7.59194 │
│ 2020-01-07T22:00:00 │  -7.6519 │
│ 2020-01-07T22:15:00 │ -7.30779 │
│ 2020-01-07T22:30:00 │ -6.96369 │
│ 2020-01-07T22:45:00 │ -6.61959 │
│ 2020-01-07T23:00:00 │ -6.27549 │
└─────────────────────┴──────────┘
                  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 │ -4.07353 │
│ 2020-01-01T06:00:00 │ -6.73018 │
│ 2020-01-01T12:00:00 │ -8.96206 │
│ 2020-01-01T18:00:00 │ -10.1172 │
│ 2020-01-02T00:00:00 │  -10.655 │
│ 2020-01-02T06:00:00 │ -11.2209 │
│ 2020-01-02T12:00:00 │ -10.0854 │
│ 2020-01-02T18:00:00 │ -8.20725 │
│          ⋮          │    ⋮     │
│ 2020-01-06T06:00:00 │ -16.3777 │
│ 2020-01-06T12:00:00 │ -14.9038 │
│ 2020-01-06T18:00:00 │  -17.809 │
│ 2020-01-07T00:00:00 │ -16.2396 │
│ 2020-01-07T06:00:00 │ -12.3551 │
│ 2020-01-07T12:00:00 │  -8.0882 │
│ 2020-01-07T18:00:00 │ -7.30912 │
└─────────────────────┴──────────┘
                   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())