Wrapper de stats::decompose() e stats::stl().
Usage
rnp_ts_decomposicao(
x,
type = c("classica", "stl"),
frequency = NULL,
digits = 4L
)See also
Other series:
rnp_arima(),
rnp_auto_arima(),
rnp_grafico_acf(),
rnp_grafico_serie(),
rnp_media_movel(),
rnp_sarima(),
rnp_suavizacao_exponencial(),
rnp_ts_acf(),
rnp_ts_adf(),
rnp_ts_ccf(),
rnp_ts_diferenciacao(),
rnp_ts_garch(),
rnp_ts_holt_winters(),
rnp_ts_kpss(),
rnp_ts_ljung_box(),
rnp_ts_pacf(),
rnp_ts_periodograma(),
rnp_ts_previsao(),
rnp_ts_residuos(),
rnp_ts_var()
Examples
x <- ts(rnorm(100) + rep(1:4, 25), frequency = 4)
rnp_ts_decomposicao(x)
#> $serie
#> $x
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 1 0.60988134 2.37637029 3.24416492 2.57374266
#> 2 2.77842929 2.13444766 3.76559900 4.95513668
#> 3 0.94943430 1.69418458 3.89367370 2.95270185
#> 4 2.97133739 1.61636789 4.65414530 5.51221269
#> 5 1.08296573 2.56722091 1.97545152 4.32300650
#> 6 2.04361246 2.09907849 2.54586309 3.34421815
#> 7 0.96407758 3.06916146 2.51602507 3.87898989
#> 8 -0.29414000 2.49431284 4.30790152 5.49704101
#> 9 1.81470273 0.13021121 3.48202950 4.45613560
#> 10 0.64659971 2.17048947 2.13596405 4.67923077
#> 11 0.67289899 0.43091781 2.63254924 5.36443493
#> 12 0.66571864 2.73275004 3.94658564 4.00439870
#> 13 0.64767769 1.47030449 3.73958923 2.93654258
#> 14 1.24621084 1.71050063 0.73511064 2.59114954
#> 15 1.91601933 1.80872105 3.80328322 5.88747446
#> 16 2.47388118 2.67726849 3.37996269 3.80720157
#> 17 2.57789179 2.59623411 1.82642306 3.84435747
#> 18 -0.91890982 1.80474115 0.40767233 5.31400217
#> 19 0.36445700 1.57002116 2.83068167 4.61221817
#> 20 1.67834018 2.56795197 2.42745740 2.63670874
#> 21 0.61127776 2.27791413 2.17691888 3.93115907
#> 22 -0.16766233 1.99169099 3.12885540 3.85412437
#> 23 0.83608904 3.76355200 3.76258651 5.11143108
#> 24 0.07679305 2.16434184 4.15482519 3.94347858
#> 25 -1.12936065 2.34484576 1.09504455 3.18882985
#>
#> $seasonal
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 1 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 2 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 3 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 4 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 5 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 6 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 7 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 8 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 9 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 10 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 11 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 12 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 13 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 14 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 15 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 16 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 17 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 18 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 19 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 20 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 21 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 22 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 23 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 24 -1.5340820 -0.4686385 0.4022118 1.6005087
#> 25 -1.5340820 -0.4686385 0.4022118 1.6005087
#>
#> $trend
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 1 NA NA 2.472108 2.712936
#> 2 2.747875 3.110729 3.179779 2.896122
#> 3 2.857098 2.622803 2.625236 2.868247
#> 4 2.953579 3.368577 3.452469 3.335280
#> 5 3.119299 2.635812 2.607242 2.668805
#> 6 2.681589 2.630542 2.373251 2.359570
#> 7 2.477100 2.540217 2.449786 2.220653
#> 8 2.372782 2.799022 3.264884 3.232977
#> 9 2.834230 2.600883 2.324757 2.433779
#> 10 2.520555 2.380184 2.411358 2.197199
#> 11 2.041826 2.189550 2.274303 2.561134
#> 12 3.013118 3.007368 2.835108 2.675047
#> 13 2.491367 2.332011 2.273345 2.378186
#> 14 2.032651 1.613917 1.654469 1.750473
#> 15 2.146272 2.941834 3.423607 3.601908
#> 16 3.657562 3.344613 3.097580 3.100452
#> 17 2.896130 2.706582 2.274126 1.738090
#> 18 1.461809 1.468171 1.812297 1.943378
#> 19 2.216914 2.432067 2.508580 2.797557
#> 20 2.871895 2.574553 2.194232 2.024594
#> 21 1.957022 2.087511 2.151950 2.018805
#> 22 2.102019 2.211381 2.327221 2.674173
#> 23 2.974872 3.211251 3.273503 2.978689
#> 24 2.827818 2.730854 2.434090 2.305884
#> 25 1.945975 1.469171 NA NA
#>
#> $random
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 1 NA NA 0.36984481 -1.73970246
#> 2 1.56463585 -0.50764272 0.18360840 0.45850649
#> 3 -0.37358172 -0.45997986 0.86622539 -1.51605410
#> 4 1.55184018 -1.28357055 0.79946413 0.57642450
#> 5 -0.50225175 0.40004749 -1.03400230 0.05369280
#> 6 0.89610572 -0.06282458 -0.22959991 -0.61586021
#> 7 0.02105922 0.99758295 -0.33597305 0.05782820
#> 8 -1.13283955 0.16392891 0.64080552 0.66355553
#> 9 0.51455457 -2.00203321 0.75506080 0.42184815
#> 10 -0.33987372 0.25894389 -0.67760618 0.88152275
#> 11 0.16515489 -1.28999339 -0.04396527 1.20279208
#> 12 -0.81331717 0.19402078 0.70926569 -0.27115728
#> 13 -0.30960743 -0.39306750 1.06403227 -1.04215238
#> 14 0.74764180 0.56522211 -1.32157015 -0.75983171
#> 15 1.30382957 -0.66447433 -0.02253584 0.68505740
#> 16 0.35040136 -0.19870558 -0.11982894 -0.89375893
#> 17 1.21584366 0.35829051 -0.84991516 0.50575922
#> 18 -0.84663699 0.80520880 -1.80683679 1.77011534
#> 19 -0.31837538 -0.39340782 -0.08011004 0.21415287
#> 20 0.34052717 0.46203724 -0.16898619 -0.98839415
#> 21 0.18833752 0.65904148 -0.37724288 0.31184586
#> 22 -0.73559909 0.24894806 0.39942256 -0.42055687
#> 23 -0.60470060 1.02093920 0.08687204 0.53223303
#> 24 -1.21694295 -0.09787337 1.31852292 0.03708569
#> 25 -1.54125333 1.34431331 NA NA
#>
#> $figure
#> [1] -1.5340820 -0.4686385 0.4022118 1.6005087
#>
#> $type
#> [1] "additive"
#>
#> attr(,"class")
#> [1] "decomposed.ts"
#>
#> $componentes
#> # A tibble: 100 × 6
#> observacao tempo observada tendencia sazonal aleatorio
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.610 NA -1.53 NA
#> 2 2 1.25 2.38 NA -0.469 NA
#> 3 3 1.5 3.24 2.47 0.402 0.370
#> 4 4 1.75 2.57 2.71 1.60 -1.74
#> 5 5 2 2.78 2.75 -1.53 1.56
#> 6 6 2.25 2.13 3.11 -0.469 -0.508
#> 7 7 2.5 3.77 3.18 0.402 0.184
#> 8 8 2.75 4.96 2.90 1.60 0.458
#> 9 9 3 0.949 2.86 -1.53 -0.374
#> 10 10 3.25 1.69 2.62 -0.469 -0.46
#> # ℹ 90 more rows
#>