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
#> -0.0370008158 -0.0362000025 -0.0353991891 -0.0345983758 -0.0337975625
#> 6 7 8 9 10
#> -0.0329967492 -0.0321959358 -0.0313951225 -0.0305943092 -0.0297934958
#> 11 12 13 14 15
#> -0.0289926825 -0.0281918692 -0.0273910558 -0.0265902425 -0.0257894292
#> 16 17 18 19 20
#> -0.0249886158 -0.0241878025 -0.0233869892 -0.0225861758 -0.0217853625
#> 21 22 23 24 25
#> -0.0209845492 -0.0201837358 -0.0193829225 -0.0185821092 -0.0177812958
#> 26 27 28 29 30
#> -0.0169804825 -0.0161796692 -0.0153788559 -0.0145780425 -0.0137772292
#> 31 32 33 34 35
#> -0.0129764159 -0.0121756025 -0.0113747892 -0.0105739759 -0.0097731625
#> 36 37 38 39 40
#> -0.0089723492 -0.0081715359 -0.0073707225 -0.0065699092 -0.0057690959
#> 41 42 43 44 45
#> -0.0049682825 -0.0041674692 -0.0033666559 -0.0025658425 -0.0017650292
#> 46 47 48 49 50
#> -0.0009642159 -0.0001634026 0.0006374108 0.0014382241 0.0022390374
#>
#> $seasonal
#> [1] 0.67603838 0.24680232 -0.04099563 0.12933868 -0.03966967 -0.48014345
#> [7] -0.33055416 0.77256834 -0.36042838 0.62007558 0.09560947 -1.28864147
#> [13] 0.67603838 0.24680232 -0.04099563 0.12933868 -0.03966967 -0.48014345
#> [19] -0.33055416 0.77256834 -0.36042838 0.62007558 0.09560947 -1.28864147
#> [25] 0.67603838 0.24680232 -0.04099563 0.12933868 -0.03966967 -0.48014345
#> [31] -0.33055416 0.77256834 -0.36042838 0.62007558 0.09560947 -1.28864147
#> [37] 0.67603838 0.24680232 -0.04099563 0.12933868 -0.03966967 -0.48014345
#> [43] -0.33055416 0.77256834 -0.36042838 0.62007558 0.09560947 -1.28864147
#> [49] 0.67603838 0.24680232
#>
#> $residual
#> 1 2 3 4 5 6
#> 0.57096639 -0.96358297 -0.57287242 -0.34898475 -1.22290233 0.08848310
#> 7 8 9 10 11 12
#> -0.03640563 -0.68129830 1.07170056 0.97913967 0.74469223 0.04556542
#> 13 14 15 16 17 18
#> 0.85602856 0.58677502 0.13136204 1.23970365 0.60575524 0.34946539
#> 19 20 21 22 23 24
#> -0.22919131 0.13580566 -1.82974404 -0.75478013 -1.20334000 -0.71289345
#> 25 26 27 28 29 30
#> -0.43563008 -0.35772496 -1.96686984 -0.39363870 0.47487837 1.13680561
#> 31 32 33 34 35 36
#> 0.63791196 -0.04125100 0.56430787 -0.24206197 -0.19923523 0.26102021
#> 37 38 39 40 41 42
#> -0.41527568 0.68311382 2.32250149 -0.56500825 0.05638998 -1.64268216
#> 43 44 45 46 47 48
#> -0.45819375 0.51881559 0.10785688 -0.05022563 0.57200426 0.33837977
#> 49 50
#> -0.66196793 -0.01650896
#>
#> $slope
#> [1] 0.0008008133
#>
#> $intercept
#> [1] -0.03700082
#>
#> $fourier_terms
#> X1 X2 X3 X4 X5 X6
#> -0.03164820 0.01307365 0.33642081 -0.10098251 0.12891149 0.09198011
#> X7 X8 X9 X10
#> 0.46845037 0.27372462 -0.08480877 0.39824251
#>
#> $period
#> [1] 12
#>
#> $n
#> [1] 50
#>
#> $method
#> [1] "Prophet-style linear-trend + Fourier(K=5) seasonality"
#>