Prophet-style additive decomposition (linear trend + Fourier seasonality)
Source:R/propc.R
morie_prophet_components.RdProphet-style additive decomposition (linear trend + Fourier seasonality)
Value
Named list with trend, seasonal, residual, slope,
intercept, fourier_terms, period, n, method.
Examples
morie_prophet_components(x = rnorm(50))
#> $trend
#> 1 2 3 4 5
#> 3.823551e-01 3.745503e-01 3.667455e-01 3.589407e-01 3.511359e-01
#> 6 7 8 9 10
#> 3.433312e-01 3.355264e-01 3.277216e-01 3.199168e-01 3.121120e-01
#> 11 12 13 14 15
#> 3.043072e-01 2.965024e-01 2.886977e-01 2.808929e-01 2.730881e-01
#> 16 17 18 19 20
#> 2.652833e-01 2.574785e-01 2.496737e-01 2.418689e-01 2.340642e-01
#> 21 22 23 24 25
#> 2.262594e-01 2.184546e-01 2.106498e-01 2.028450e-01 1.950402e-01
#> 26 27 28 29 30
#> 1.872355e-01 1.794307e-01 1.716259e-01 1.638211e-01 1.560163e-01
#> 31 32 33 34 35
#> 1.482115e-01 1.404067e-01 1.326020e-01 1.247972e-01 1.169924e-01
#> 36 37 38 39 40
#> 1.091876e-01 1.013828e-01 9.357804e-02 8.577325e-02 7.796847e-02
#> 41 42 43 44 45
#> 7.016368e-02 6.235890e-02 5.455411e-02 4.674933e-02 3.894454e-02
#> 46 47 48 49 50
#> 3.113976e-02 2.333497e-02 1.553019e-02 7.725404e-03 -7.938051e-05
#>
#> $seasonal
#> [1] 0.3155939 -0.5628486 0.3561962 -0.3951792 0.1149072 -0.3844554
#> [7] -0.1223838 0.1884018 0.6577588 0.5367505 -1.3220723 0.6173309
#> [13] 0.3155939 -0.5628486 0.3561962 -0.3951792 0.1149072 -0.3844554
#> [19] -0.1223838 0.1884018 0.6577588 0.5367505 -1.3220723 0.6173309
#> [25] 0.3155939 -0.5628486 0.3561962 -0.3951792 0.1149072 -0.3844554
#> [31] -0.1223838 0.1884018 0.6577588 0.5367505 -1.3220723 0.6173309
#> [37] 0.3155939 -0.5628486 0.3561962 -0.3951792 0.1149072 -0.3844554
#> [43] -0.1223838 0.1884018 0.6577588 0.5367505 -1.3220723 0.6173309
#> [49] 0.3155939 -0.5628486
#>
#> $residual
#> 1 2 3 4 5 6
#> 1.610029435 0.294100681 -0.265942942 -0.040914449 -0.800043994 0.006398171
#> 7 8 9 10 11 12
#> 0.574497054 1.559121619 0.049716830 0.359045888 -0.213558311 0.070062267
#> 13 14 15 16 17 18
#> -0.384366742 -1.185294298 -0.108261587 -0.028858701 1.092201578 -0.631300382
#> 19 20 21 22 23 24
#> -0.549696887 -1.348575469 -1.061122153 -0.353193313 0.379674355 0.010197283
#> 25 26 27 28 29 30
#> -1.718716914 -0.672371264 0.905530795 -0.792294143 0.133246396 -0.152637016
#> 31 32 33 34 35 36
#> 0.383574124 1.360064733 0.796227660 -0.992455475 -1.080155589 1.771143123
#> 37 38 39 40 41 42
#> 0.250089457 1.010597903 -0.455369017 0.827319163 -0.349446730 0.742791097
#> 43 44 45 46 47 48
#> -0.332417041 -1.605359013 0.291134912 0.951854769 0.989996795 -1.886150804
#> 49 50
#> 0.318922014 0.518218848
#>
#> $slope
#> [1] -0.007804785
#>
#> $intercept
#> [1] 0.3823551
#>
#> $fourier_terms
#> X1 X2 X3 X4 X5 X6
#> -0.13752468 -0.03572050 0.23293166 0.01290970 -0.13685115 0.36275330
#> X7 X8 X9 X10
#> -0.40825051 0.08369534 -0.46529133 -0.10804395
#>
#> $period
#> [1] 12
#>
#> $n
#> [1] 50
#>
#> $method
#> [1] "Prophet-style linear-trend + Fourier(K=5) seasonality"
#>