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Interpolate missing values

Source: R/prepare_data.R
interpolate_missing.Rd

Interpolate missing values in a numeric time series.

Usage

interpolate_missing(x, periods, ...)

Arguments

x

Numeric vector containing the time series observations.

periods

Numeric vector giving the seasonal periods of the time series, for example 12 for monthly data with yearly seasonality or c(24, 168) for hourly data with daily and weekly seasonality.

...

Further arguments passed to forecast::msts() or forecast::na.interp().

Value

A numeric vector with missing values interpolated.

Details

interpolate_missing() is a small wrapper around forecast::na.interp(). The input vector is first converted to an msts object using the seasonal periods supplied in periods.

For non-seasonal time series, missing values are replaced using linear interpolation. For seasonal time series, forecast::na.interp() uses an STL-based approach: the series is decomposed, the seasonally adjusted series is interpolated, and the seasonal component is added back.

The function returns a plain numeric vector with the same length as the input.

See also

Other data preparation: check_data(), smooth_outlier()

Examples

library(dplyr)

x <- M4_monthly_data |>
  filter(series == first(series)) |>
  pull(value)

x_missing <- x
x_missing[c(10, 20, 30)] <- NA

x_interpolated <- interpolate_missing(
  x = x_missing,
  periods = 12
)

anyNA(x_missing)
#> [1] TRUE
anyNA(x_interpolated)
#> [1] FALSE

hourly <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 14) |>
  pull(value)

hourly_missing <- hourly
hourly_missing[c(24, 48, 72)] <- NA

interpolate_missing(
  x = hourly_missing,
  periods = c(24, 168)
)
#>   [1]  10.070  -4.080  -9.910  -7.410 -12.550 -17.250 -15.070  -4.930  -6.330
#>  [10]  -4.930   0.450   0.120  -0.020   0.000  -0.030   1.970   9.060   0.070
#>  [19]  -4.970  -6.980 -24.930  -4.870 -28.930 -37.425 -45.920 -48.290 -44.990
#>  [28] -48.930 -29.910  -0.010  37.430  48.060  50.740  47.570  43.940  40.970
#>  [37]  44.950  49.640  53.670  56.010  56.950  62.080  62.110  57.990  55.640
#>  [46]  55.130  50.760  47.990  45.220  45.630  44.000  43.880  45.920  51.070
#>  [55]  52.770  62.890  60.030  58.190  62.990  63.520  64.670  65.240  67.760
#>  [64]  68.410  69.550  67.280  69.460  68.380  61.720  53.720  49.980  48.545
#>  [73]  47.110  47.070  46.940  47.000  46.910  49.590  55.320  55.780  55.520
#>  [82]  55.230  53.580  51.740  51.600  51.410  51.690  52.590  54.660  54.100
#>  [91]  51.890  46.580  45.430  43.960  31.410  26.900  25.120  24.120  22.040
#> [100]  18.370  22.090  23.350  28.760  36.630  40.460  45.850  49.800  51.360
#> [109]  51.740  51.920  53.220  56.620  56.920  61.640  59.440  52.750  51.900
#> [118]  51.380  49.960  50.290  47.720  48.380  48.020  44.230  47.170  48.190
#> [127]  49.110  50.440  53.400  56.550  60.020  60.220  55.000  52.390  51.570
#> [136]  54.710  63.430  67.370  67.200  66.030  55.360  58.590  53.700  46.030
#> [145]  47.980  47.840  46.110  46.080  47.620  55.770  68.610  74.150  74.930
#> [154]  73.590  71.230  68.790  66.750  62.470  53.250  53.260  53.420  47.910
#> [163]  42.050  40.960  32.040  20.820   1.840  17.940  20.910   7.780  14.330
#> [172]  18.560  18.570  35.810  43.870  46.930  43.880  43.850  46.740  43.940
#> [181]  43.210  43.810  45.600  35.210  45.640  45.630  37.940  39.530  35.970
#> [190]  29.720  22.550  20.040   7.240   3.430  10.040  14.200  25.410  36.980
#> [199]  43.890  50.980  50.300  50.210  45.800  43.640  43.490  45.050  49.950
#> [208]  53.820  55.940  54.190  54.190  52.980  51.680  48.950  47.630  46.480
#> [217]  49.080  47.110  48.800  48.500  49.800  56.970  72.600  81.170  81.790
#> [226]  81.150  80.000  76.390  75.000  76.060  75.400  76.780  83.450  85.150
#> [235]  78.160  71.920  67.430  60.490  54.450  47.840  46.580  45.740  43.810
#> [244]  46.240  46.460  51.290  60.530  60.020  62.920  61.580  60.600  51.890
#> [253]  50.800  45.460  42.200  45.000  51.510  48.590  49.200  44.990  44.930
#> [262]  46.400  43.460  47.240  45.880  44.610  42.900  42.030  39.330  42.980
#> [271]  41.230  42.120  45.100  44.270  44.180  42.050  38.610  37.760  33.030
#> [280]  35.840  44.240  46.290  40.050  38.150  30.260  40.200  30.890  23.350
#> [289]  12.400  19.010  23.510  23.250  20.010  20.800  22.140  23.680  25.050
#> [298]  26.300  30.810  27.060   8.500  -0.080  -0.010  -0.270  21.550  24.040
#> [307]  15.910  -0.020  -0.330   3.040  -7.280  -3.140 -12.000 -15.040 -11.090
#> [316]  -4.940   1.590  35.900  51.220  51.660  46.090  50.260  44.490  41.240
#> [325]  38.910  41.790  49.630  50.930  51.250  50.930  50.520  49.610  44.300
#> [334]  41.290  37.260  35.180