Professor: ADRIANO
Matricula: 162900029
Disciplina: Programação no Rstudio/Rmarkdown

1 - Projeto base de dados PCAR( Pão de açucar)

Base de dados referente as ações do banco de dados do pão de açucar(PCAR) referente ao periodo de 2019 até 2020.
Objetivo deste projeto é realizar a previsão da padronização ao longo do periodo de 1 ano.

2 - Pacotes instalados.

Pacotes necessários para realização deste projeto.

library(neuralnet)
library(quantmod)
library(zoo)
library(forecast)
library(DMwR2)
library(stats)
library(plogr)
library(ggplot2)
library(psych)

3 - Padronização.

Utilizado para manter a estabilidade e garantir que as atividades sejam realizadas sempre da mesma forma.

PadraodeDados <- function(vetorDados){
    
    if(!is.numeric(vetorDados)){
      stop("Dados não são numéricos!")
    }
    
  PadraodeDados <- (vetorDados - mean(vetorDados, na.rm = TRUE)) / sd(vetorDados, na.rm = TRUE)

      return(PadraodeDados)
}

4 - Despadronização.

Tirar dos padrões os moldes considerados corretos ou apropriados.

DesPadronizacao <- function(vetorDados, media_orig, desvpad_orig){
  # Valida dados
  if(!is.numeric(vetorDados)){
    stop("Dados não são numéricos!")
  }
  # DesPadroniza dados
  dadosDesPadronizados <- vetorDados * desvpad_orig + media_orig
  # Retorno da funcao
  return(dadosDesPadronizados)
}

5 - Dados carregados.

getSymbols("PCAR" , src= "yahoo" , from="2019-01-01" , to= "2020-12-31")
## [1] "PCAR"
names(PCAR) <- c("open", "high", "low", "close", "volume", "adjusted")
dat_a = data.frame(PCAR)
head(dat_a)
##             open  high   low close  volume adjusted
## 2019-01-02 56.27 57.95 55.84 57.86 1803000 51.81023
## 2019-01-03 57.46 57.58 56.11 56.52 1974700 50.61033
## 2019-01-04 57.20 59.06 57.00 59.03 2569900 52.85789
## 2019-01-07 58.76 59.73 57.82 58.96 3047700 52.79520
## 2019-01-08 59.50 60.19 58.98 59.53 2966300 53.30561
## 2019-01-09 59.81 60.16 58.31 58.63 3471500 52.49971

6 - Analise de dados.

Identificando a correlação entre variáveis.

pairs.panels(dat_a, method = 'pearson') 

pairs.panels(dat_a, method = 'spearman')

pairs.panels(dat_a, method = 'kendall')

7 - Criando as variávies explicativas ou independentes.

Deslocamento de 1 dia, para prever o fechamento.

dat_a['closem1'] <- as.numeric(Lag(Cl(PCAR),1))

8 - Preenchimento da NA existentes nas tabelas.

dat_a <- na.fill(dat_a,"extend")
head(dat_a)
##       open  high   low close  volume adjusted closem1
## [1,] 56.27 57.95 55.84 57.86 1803000 51.81023   57.86
## [2,] 57.46 57.58 56.11 56.52 1974700 50.61033   57.86
## [3,] 57.20 59.06 57.00 59.03 2569900 52.85789   56.52
## [4,] 58.76 59.73 57.82 58.96 3047700 52.79520   59.03
## [5,] 59.50 60.19 58.98 59.53 2966300 53.30561   58.96
## [6,] 59.81 60.16 58.31 58.63 3471500 52.49971   59.53

9 - Normalizando os dados.

Padronizando os dados a partir das colunas.

dat_scale_a <- matrix(0, nrow = dim(dat_a)[1], ncol = dim(dat_a)[2])
colnames(dat_scale_a) <- c("open", "high", "low", "close", "volume", "adjusted", "closem1")
dat_scale_a[,1] <- PadraodeDados(dat_a[,1])
dat_scale_a[,2] <- PadraodeDados(dat_a[,2])
dat_scale_a[,3] <- PadraodeDados(dat_a[,3])
dat_scale_a[,4] <- PadraodeDados(dat_a[,4])
dat_scale_a[,5] <- PadraodeDados(dat_a[,5])
dat_scale_a[,6] <- PadraodeDados(dat_a[,6])
dat_scale_a[,7] <- PadraodeDados(dat_a[,7])

head(dat_scale_a)
##           open      high       low     close      volume  adjusted   closem1
## [1,] -2.011671 -1.929870 -1.976916 -1.861946 -0.01266213 -1.836724 -1.852503
## [2,] -1.877969 -1.971773 -1.946439 -2.014279  0.22288464 -1.966025 -1.852503
## [3,] -1.907181 -1.804160 -1.845978 -1.728939  1.03941020 -1.723828 -2.004580
## [4,] -1.731909 -1.728280 -1.753418 -1.736896  1.69488049 -1.730584 -1.719720
## [5,] -1.648766 -1.676184 -1.622479 -1.672098  1.58321184 -1.675581 -1.727664
## [6,] -1.613936 -1.679582 -1.698107 -1.774411  2.27627083 -1.762425 -1.662975

10 - Testando a padronização e despadronização.

Teste realizado com o objeitvo de identificar os padrões e seus devios.

dat_a1 <- matrix(0, nrow = dim(dat_a)[1], ncol = dim(dat_a)[2])
colnames(dat_a1) <- c("open", "high", "low", "close", "volume", "adjusted", "closem1")
dat_a1[,1] <- DesPadronizacao(dat_scale_a[,1], mean(dat_a[,1]), sd(dat_a[,1]))
dat_a1[,2] <- DesPadronizacao(dat_scale_a[,2], mean(dat_a[,2]), sd(dat_a[,2]))
dat_a1[,3] <- DesPadronizacao(dat_scale_a[,3], mean(dat_a[,3]), sd(dat_a[,3]))
dat_a1[,4] <- DesPadronizacao(dat_scale_a[,4], mean(dat_a[,4]), sd(dat_a[,4]))
dat_a1[,5] <- DesPadronizacao(dat_scale_a[,5], mean(dat_a[,5]), sd(dat_a[,5]))
dat_a1[,6] <- DesPadronizacao(dat_scale_a[,6], mean(dat_a[,6]), sd(dat_a[,6]))
dat_a1[,7] <- DesPadronizacao(dat_scale_a[,7], mean(dat_a[,7]), sd(dat_a[,7]))

11 - Construindo o modelo para depois realizar as previsões.

criação dos moldes que visam realizar as previsões satisfatórias

nn_a <- neuralnet(closem1 ~ open + high + low + close + volume + adjusted,
                  data = dat_scale_a, hidden  = c(5,5), threshold = 1, stepmax = 1000)

print(nn_a)
## $call
## neuralnet(formula = closem1 ~ open + high + low + close + volume + 
##     adjusted, data = dat_scale_a, hidden = c(5, 5), threshold = 1, 
##     stepmax = 1000)
## 
## $response
##          closem1
## 1   -1.852503023
## 2   -1.852503023
## 3   -2.004579702
## 4   -1.719719981
## 5   -1.727664279
## 6   -1.662974994
## 7   -1.765115744
## 8   -1.704966171
## 9   -1.673189092
## 10  -1.613039179
## 11  -1.658435282
## 12  -1.602825081
## 13  -1.457558028
## 14  -1.271434471
## 15  -1.376980260
## 16  -1.425780949
## 17  -1.406487426
## 18  -1.304346677
## 19  -1.462097741
## 20  -1.060343574
## 21  -1.070557217
## 22  -0.983170278
## 23  -1.046724436
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## 
## $covariate
##                 open         high         low         close       volume
##   [1,] -2.0116709004 -1.929869995 -1.97691641 -1.8619456643 -0.012662128
##   [2,] -1.8779692078 -1.971773363 -1.94643918 -2.0142788614  0.222884644
##   [3,] -1.9071811422 -1.804159553 -1.84597768 -1.7289386471  1.039410203
##   [4,] -1.7319085249 -1.728280390 -1.75341754 -1.7368963455  1.694880486
##   [5,] -1.6487660014 -1.676184284 -1.62247881 -1.6720979443  1.583211836
##   [6,] -1.6139360071 -1.679581750 -1.69810710 -1.7744109820  2.276270828
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## [390,]  0.5421459277  0.544700654  0.58203303  0.6185824917  0.655703552
## [391,]  0.5556287997  1.011301010  0.62718454  1.0710349407  2.576294585
## [392,]  1.0848181840  1.292168037  1.16448418  1.2517879208  1.440264989
## [393,]  1.2533498709  1.302360323  1.31461244  1.3438696320  1.129129241
## [394,]  1.3848043619  1.387299811  1.32815816  1.3074915822  1.188667563
## [395,]  1.2994147492  1.347661497  1.34508922  1.3279542352 -0.309256257
## [396,]  1.3252566116  1.318215581  1.31574145  1.2745238609  0.253614100
## [397,]  1.2230137178  1.269516941  1.31574145  1.3359119336 -0.231060765
## [398,]  1.2308784175  1.148336604  1.20512045  1.1790319349 -0.787346240
## [399,]  1.1297600795  1.143806385  1.16787041  1.2324623092 -0.308707517
## [400,]  1.2129021537  1.267252228  1.28639288  1.2927133405 -0.690219207
## [401,]  1.1836897699  1.167590235  1.25817333  1.2131366976 -0.544528659
## [402,]  1.2836851251  1.356721935  1.31461244  1.4393624674 -0.472369310
## [403,]  1.3881748833  1.404287822  1.48731600  1.4916558003 -0.517640385
## [404,]  1.4477226336  1.544720883  1.51892179  1.6326204021 -0.618059859
## [405,]  1.6185018590  1.772358938  1.58100515  1.7747221591  0.618252027
## [406,]  1.8319754414  1.847106140  1.85755644  1.8361102318 -0.397054705
## [407,]  1.8465812962  1.811997252  1.86432970  1.8292896885 -0.324483801
## [408,]  1.7802917166  1.702142068  1.68146665  1.6212524889 -0.359603180
## [409,]  1.6095132777  1.638720810  1.65437610  1.6724087804 -0.612709641
## [410,]  1.6937791211  1.646648495  1.69501237  1.6337575572  0.061006257
## [411,]  1.6611962160  1.640986316  1.69614048  1.6280731459 -1.132778255
## [412,]  1.6050189870  1.592287676  1.62615655  1.5769168544 -0.562637089
## [413,]  1.5162588529  1.513010254  1.52795216  1.4711930336 -1.135659142
## [414,]  1.4454758815  1.409950794  1.47941452  1.4530036676 -0.702154309
## [415,]  1.4690702053  1.463179767  1.50650507  1.4393624674 -0.159998897
## [416,]  1.4926645291  1.455251175  1.44667973  1.3836588060 -0.668955521
## [417,]  1.3364919450  1.387299811  1.36202106  1.4359521389 -0.972820459
## [418,]  1.4904177770  1.422408699  1.42410442  1.3745640662 -0.969253647
## [419,]  1.3387385847  1.303493076  1.37330877  1.3927526364 -0.908617845
## [420,]  1.3252566116  1.322745800  1.39249863  1.3188594954 -0.516680089
## [421,]  1.3263803809  1.374841906  1.31574145  1.4564147919 -0.112258491
## [422,]  1.4454758815  1.496022243  1.54488412  1.5507696764 -0.380729681
## [423,]  1.6499609949  1.590022170  1.29090812  1.2745238609 -0.276606210
## [424,]  1.3724452592  1.334070952  1.31574145  1.2938503819 -0.441502669
## [425,]  1.1960488726  1.160794509  1.12949227  1.0517084196 -0.081940590
## [426,]  1.0904353451  1.191373178  1.15093936  1.2188201995  0.434972768
## [427,]  1.1645897368  1.204962930  1.01661405  0.9778152786 -0.543842734
## [428,]  0.9971818192  1.046409898  1.07756840  1.0971810955  1.413925455
## [429,]  1.1758249579  1.313685362  1.22430952  1.3779743947 -0.357682588
## [430,]  1.3724452592  1.357854688  1.38911251  1.3109019107 -0.094973172
## [431,]  1.4151397285  1.392962783  1.41055870  1.3847949380 -0.663193748
## [432,]  1.2207669657  1.348794363  1.22769655  1.3211336919 -0.261378667
## [433,]  1.3218860902  1.274047160  1.19947699  1.1369694738  0.582858277
## [434,]  1.0376295364  0.972795562  0.79085762  0.8664080696 -0.200880049
## [435,]  0.8207853203  0.926361522  0.85971333  0.9209745759 -0.644399393
## [436,]  0.9443751117  0.969398095  0.88228863  0.8379877182 -0.475387382
## [437,]  0.7949434579  0.929758988  0.79650187  0.9005128323 -0.985578671
## [438,]  0.9398808210  1.031686487  0.95453069  1.0562557895 -0.849628263
## [439,]  1.1791954792  1.320481200  1.27736240  1.3995740891 -0.307198481
## [440,]  1.3578395167  1.382769592  1.40265733  1.3541014132 -0.949636182
## [441,]  1.3881748833  1.439395917  1.27849141  1.2551982493  0.236465966
## [442,]  1.3297509022  1.271782447  1.15206758  1.1653898252 -0.927686570
## [443,]  1.0533590482  1.228745873  1.07192404  1.2222305280 -1.060893277
## [444,]  1.3207623209  1.522070692  1.39136963  1.5746426579 -0.845512711
## [445,]  1.5623246301  1.682889230  1.59116373  1.5280337364 -0.766905664
## [446,]  1.6398486443  1.699877354  1.69501237  1.7792695290 -1.031810041
## [447,]  1.7342268383  1.695347135  1.65324709  1.7849539403 -1.416751358
## [448,]  1.7780449646  1.808599786  1.74693624  1.7031032146  0.034803908
## [449,]  1.8027630576  1.730455230  1.78079914  1.7667645744 -0.090446065
## [450,]  1.6589494639  1.688552316  1.60809558  1.6257998588 -0.199096643
## [451,]  1.5825484325  1.699877354  1.68259566  1.6746828632 -0.863483956
## [452,]  1.5510892967  1.613805113  1.63744426  1.6962826713 -1.213031524
## [453,]  1.7342268383  1.850503607  1.64760284  1.7849539403  0.145375077
## [454,]  1.7746744432  1.881081369  1.87561730  1.9122766598  1.075489878
## [455,]  1.9330937794  1.926382656  1.76612531  1.8304258205  2.903755356
## [456,]  1.7881564163  1.901466960  1.88126076  1.8952251311  0.996471275
## [457,]  2.0050006324  1.980744268  1.94673024  1.9838964001 -0.112670047
## [458,]  2.0106186924  2.035105088  2.07541140  2.0918933943 -0.350960520
## [459,]  1.9196118062  1.860695892  1.82143541  1.8565729985  0.116017471
## [460,]  1.8634345773  1.857298426  1.69501237  1.6155680776 -0.648377760
## [461,]  1.4173872671  1.419010440  1.30219572  1.2108625011  0.058811296
## [462,]  1.1769488395  1.312553515  1.22656754  1.2995338838 -0.755244932
## [463,]  1.2218907350  1.233276093  1.24349950  1.2665661625 -0.623410076
## [464,]  1.4016577553  1.576432418  1.47038404  1.6587675801 -0.430116307
## [465,]  1.7881564163  1.963756257  1.82595065  1.9941273855 -0.235039133
## [466,]  2.0836492024  2.073611442  1.72210292  1.6496728403 -0.398975296
## [467,]  1.6881610612  1.848238100  1.66340568  1.8611203684 -0.814508884
## [468,]  1.9611823939  1.884478836  1.84175349  1.8020054691 -0.934271454
## [469,]  2.3398172540  2.359007783  1.92754116  1.8599832133  0.880001147
## [470,]  1.7656858618  1.995466886  1.84401151  1.9190981126  0.291614366
## [471,]  1.9735414966  1.913924751  1.83611015  1.8111002089 -0.435054970
## [472,]  1.7499563501  1.716865478  1.70065572  1.6769561503 -0.671424852
## [473,]  1.7589449314  1.720262831  1.76951154  1.7815437255 -0.742349535
## [474,]  1.8454574146  1.842575921  1.65324709  1.7826798575 -0.584861071
## [475,]  1.7095086329  1.630793124  1.65663333  1.6576305387 -1.017542793
## [476,]  1.7151266928  1.807467033  1.69726949  1.7735851177 -1.068987196
## [477,]  1.6971496425  1.725925804  1.65550432  1.7963219672 -1.083803184
## [478,]  1.5701894421  1.677227164  1.62615655  1.6530831688 -0.208425228
## [479,]  1.8600640559  1.782552129  1.70968620  1.7417545515 -0.916437394
## [480,]  1.6465904735  1.792744528  1.74693624  1.8383844283  0.227548936
## [481,]  1.8443336453  1.787081556  1.71533045  1.6576305387 -0.696255351
## [482,]  1.6488371133  1.663636506  1.60470946  1.5348543933 -1.676168333
## [483,]  1.4679464360  1.462047014  1.46925594  1.4575510375  0.385311771
## [484,]  1.5477179888  1.573034045  1.56971744  1.4825610604 -0.538080961
## [485,]  1.4409815909  1.473372052  1.48618699  1.4677828188 -0.756479598
## [486,]  1.4510930426  1.422408699  1.43652114  1.3654693264  0.354033574
## [487,]  1.3657043288  1.404287822  1.39362685  1.4166256178  0.316170494
## [488,]  1.3746929101  1.338601171  1.39927110  1.3722899834 -0.329696833
## [489,]  1.3466042956  1.344264144  1.43990737  1.3836588060 -1.271746735
## [490,]  1.3746929101  1.426938012  1.43990737  1.4939289736 -0.129818181
## [491,]  1.4117692071  1.375974659  1.37218055  1.3086286236 -1.065146014
## [492,]  1.2072840937  1.238939065  1.27849141  1.2756609023 -0.933448343
## [493,]  1.2915499371  1.295565390  1.24462760  1.1619794967 -0.991614815
## [494,]  1.3466042956  1.419010440  1.38685438  1.4836981019 -0.115688118
## [495,]  1.4319930095  1.425806165  1.49521736  1.4382253123 -0.548095471
## [496,]  1.3825576098  1.345396897  1.33267340  1.3109019107 -0.148063796
## [497,]  1.3589624995  1.311420762  1.31461244  1.3381860164  2.614020480
## [498,]  1.1825660006  1.267252228  1.20963569  1.3199965368 -0.852509150
## [499,]  1.2937974757  1.212890615  1.27623429  1.2085893277 -1.228121882
## [500,]  1.2185194272  1.190240425  1.27849141  1.2335984412 -1.180930216
## [501,]  1.2612146829  1.269516941  1.31461244  1.3495540433 -1.974408683
## [502,]  1.4117692071  1.357854688  1.35750582  1.2893030120 -1.681106995
## [503,]  1.3297509022  1.292168037  1.23559802  1.2597456192 -1.318801216
## [504,]  1.2814384853  1.255927189  1.33380151  1.3222707333 -1.715677634
##           adjusted
##   [1,] -1.83672403
##   [2,] -1.96602462
##   [3,] -1.72382807
##   [4,] -1.73058354
##   [5,] -1.67558100
##   [6,] -1.76242523
##   [7,] -1.71128372
##   [8,] -1.68426603
##   [9,] -1.63312420
##  [10,] -1.67172136
##  [11,] -1.62444068
##  [12,] -1.50093014
##  [13,] -1.34268144
##  [14,] -1.43241967
##  [15,] -1.47391072
##  [16,] -1.45750771
##  [17,] -1.37066434
##  [18,] -1.50478968
##  [19,] -1.16320498
##  [20,] -1.17188969
##  [21,] -1.09759056
##  [22,] -1.15162552
##  [23,] -1.12364219
##  [24,] -1.09276496
##  [25,] -1.09373059
##  [26,] -1.06092166
##  [27,] -1.05609821
##  [28,] -1.08227764
##  [29,] -0.94652901
##  [30,] -0.88156363
##  [31,] -0.93586379
##  [32,] -0.81659910
##  [33,] -0.80496285
##  [34,] -0.72545342
##  [35,] -0.71284830
##  [36,] -0.64303350
##  [37,] -0.68181977
##  [38,] -0.69733398
##  [39,] -0.78556971
##  [40,] -0.84568655
##  [41,] -0.84859693
##  [42,] -0.87671583
##  [43,] -0.84665585
##  [44,] -0.89804681
##  [45,] -0.91162285
##  [46,] -0.89319945
##  [47,] -0.81174969
##  [48,] -0.80302425
##  [49,] -0.75454291
##  [50,] -0.78266095
##  [51,] -0.86023189
##  [52,] -0.78654106
##  [53,] -0.81659910
##  [54,] -0.85150688
##  [55,] -0.80787205
##  [56,] -0.92034829
##  [57,] -0.88447196
##  [58,] -0.88253379
##  [59,] -0.84374838
##  [60,] -0.80011592
##  [61,] -0.81272017
##  [62,] -0.72448326
##  [63,] -0.78556971
##  [64,] -0.79429763
##  [65,] -0.72739278
##  [66,] -0.72739278
##  [67,] -0.73514891
##  [68,] -0.78750961
##  [69,] -0.75066312
##  [70,] -0.66048717
##  [71,] -0.58388640
##  [72,] -0.59746287
##  [73,] -0.49371206
##  [74,] -0.52376881
##  [75,] -0.49274028
##  [76,] -0.52280112
##  [77,] -0.51795128
##  [78,] -0.48886308
##  [79,] -0.61685353
##  [80,] -0.53928431
##  [81,] -0.50437686
##  [82,] -0.47043956
##  [83,] -0.54994997
##  [84,] -0.61394433
##  [85,] -0.49855771
##  [86,] -0.68763763
##  [87,] -0.74387553
##  [88,] -0.74484688
##  [89,] -0.72836294
##  [90,] -0.67988031
##  [91,] -0.83086901
##  [92,] -0.72663975
##  [93,] -0.68182915
##  [94,] -0.61169171
##  [95,] -0.67501074
##  [96,] -0.66721603
##  [97,] -0.58636552
##  [98,] -0.77826747
##  [99,] -0.88736715
## [100,] -0.83866210
## [101,] -0.88347141
## [102,] -0.84353167
## [103,] -0.79190309
## [104,] -1.00815858
## [105,] -0.96821798
## [106,] -0.73540463
## [107,] -0.68475149
## [108,] -0.62630494
## [109,] -0.57954669
## [110,] -0.58149520
## [111,] -0.62143365
## [112,] -0.61851217
## [113,] -0.60389948
## [114,] -0.59902819
## [115,] -0.67501074
## [116,] -0.50453968
## [117,] -0.48408306
## [118,] -0.38374782
## [119,] -0.47044538
## [120,] -0.46459994
## [121,] -0.47434231
## [122,] -0.51233116
## [123,] -0.53376150
## [124,] -0.43927300
## [125,] -0.38277442
## [126,] -0.46947113
## [127,] -0.48408306
## [128,] -0.54155589
## [129,] -0.53668471
## [130,] -0.53960867
## [131,] -0.55129578
## [132,] -0.52499587
## [133,] -0.39348943
## [134,] -0.45485919
## [135,] -0.35063112
## [136,] -0.58733978
## [137,] -0.69059532
## [138,] -0.66624296
## [139,] -0.68280298
## [140,] -0.67598295
## [141,] -0.39251604
## [142,] -0.43829960
## [143,] -0.43829960
## [144,] -0.45388655
## [145,] -0.50648734
## [146,] -0.58733978
## [147,] -0.78216321
## [148,] -0.87178139
## [149,] -1.14356066
## [150,] -1.03640701
## [151,] -1.06270907
## [152,] -0.94191678
## [153,] -1.06757896
## [154,] -1.10946636
## [155,] -0.92542378
## [156,] -1.21910915
## [157,] -1.18386624
## [158,] -1.10750847
## [159,] -1.06835063
## [160,] -1.11729695
## [161,] -1.05268707
## [162,] -1.11142436
## [163,] -1.29840404
## [164,] -1.23379264
## [165,] -1.21029816
## [166,] -1.16526697
## [167,] -1.01940345
## [168,] -1.00178242
## [169,] -1.13100414
## [170,] -1.01157123
## [171,] -0.74333824
## [172,] -0.72963192
## [173,] -0.52111490
## [174,] -0.38014519
## [175,] -0.34000986
## [176,] -0.45063166
## [177,] -0.41734556
## [178,] -0.47412614
## [179,] -0.48000036
## [180,] -0.49370463
## [181,] -0.53286214
## [182,] -0.53579838
## [183,] -0.52992622
## [184,] -0.74431746
## [185,] -0.72278054
## [186,] -0.68851609
## [187,] -0.66404379
## [188,] -0.56614781
## [189,] -0.80794727
## [190,] -0.93717103
## [191,] -0.86179063
## [192,] -0.77858115
## [193,] -0.78934746
## [194,] -0.90388525
## [195,] -0.87158031
## [196,] -0.82948623
## [197,] -0.65523279
## [198,] -0.61803048
## [199,] -0.53286214
## [200,] -0.56027445
## [201,] -0.49076709
## [202,] -0.36742013
## [203,] -0.27344004
## [204,]  0.05646563
## [205,] -0.06688219
## [206,] -0.04828120
## [207,]  0.10835121
## [208,]  0.16806589
## [209,]  0.14946661
## [210,]  0.06625573
## [211,]  0.02611868
## [212,]  0.22093144
## [213,]  0.34329929
## [214,]  0.37952045
## [215,]  0.38049880
## [216,]  0.39909980
## [217,]  0.45020919
## [218,]  0.43546729
## [219,]  0.38730449
## [220,]  0.35880240
## [221,]  0.39713328
## [222,]  0.46495270
## [223,]  0.44627853
## [224,]  0.38730449
## [225,]  0.33029794
## [226,]  0.42268879
## [227,]  0.48657754
## [228,]  0.59273021
## [229,]  0.61828410
## [230,]  0.65563415
## [231,]  0.57798508
## [232,]  0.51213305
## [233,]  0.45217656
## [234,]  0.42760264
## [235,]  0.45414070
## [236,]  0.55341289
## [237,]  0.54849979
## [238,]  0.56127668
## [239,]  0.58093577
## [240,]  0.73328149
## [241,]  0.64089139
## [242,]  0.72050449
## [243,]  0.65465159
## [244,]  0.70084788
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## [472,]  1.72285882
## [473,]  1.81737739
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## [476,]  1.81018441
## [477,]  1.83073306
## [478,]  1.70128247
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## [480,]  1.86874648
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## 
## $model.list
## $model.list$response
## [1] "closem1"
## 
## $model.list$variables
## [1] "open"     "high"     "low"      "close"    "volume"   "adjusted"
## 
## 
## $err.fct
## function (x, y) 
## {
##     1/2 * (y - x)^2
## }
## <bytecode: 0x0000000022b92970>
## <environment: 0x0000000022b90b40>
## attr(,"type")
## [1] "sse"
## 
## $act.fct
## function (x) 
## {
##     1/(1 + exp(-x))
## }
## <bytecode: 0x0000000022a68bf8>
## <environment: 0x0000000022a68300>
## attr(,"type")
## [1] "logistic"
## 
## $linear.output
## [1] TRUE
## 
## $data
##              open         high         low         close       volume
## 1   -2.0116709004 -1.929869995 -1.97691641 -1.8619456643 -0.012662128
## 2   -1.8779692078 -1.971773363 -1.94643918 -2.0142788614  0.222884644
## 3   -1.9071811422 -1.804159553 -1.84597768 -1.7289386471  1.039410203
## 4   -1.7319085249 -1.728280390 -1.75341754 -1.7368963455  1.694880486
## 5   -1.6487660014 -1.676184284 -1.62247881 -1.6720979443  1.583211836
## 6   -1.6139360071 -1.679581750 -1.69810710 -1.7744109820  2.276270828
## 7   -1.8363977213 -1.738473242 -1.78163710 -1.7141599507  1.384430664
## 8   -1.6993256198 -1.730545556 -1.68456183 -1.6823292708  0.146746928
## 9   -1.6925841276 -1.660328573 -1.65859984 -1.6220778985  0.549522305
## 10  -1.5914654525 -1.676184284 -1.58861545 -1.6675505744 -0.566478271
## 11  -1.6285421989 -1.651268587 -1.56829738 -1.6118465720 -0.397603445
## 12  -1.6139360071 -1.485919830 -1.54572162 -1.4663344865  0.876434335
## 13  -1.3824860485 -1.338691497 -1.34705618 -1.2798969814  0.593284343
## 14  -1.3240617304 -1.405510108 -1.34479850 -1.3856208023  0.722375498
## 15  -1.3072083370 -1.410040327 -1.36850282 -1.4345038066  0.120544579
## 16  -1.4622576012 -1.479124558 -1.38994956 -1.4151777403  0.945849983
## 17  -1.3049612478 -1.327366005 -1.23417796 -1.3128647027  0.562692072
## 18  -1.3937213819 -1.489317296 -1.39898005 -1.4708818564  3.540980024
## 19  -1.1690124661 -1.134836386 -1.23530652 -1.0684500217  2.899639804
## 20  -1.0308164829 -1.109920689 -1.09759498 -1.0786808935  1.414062640
## 21  -1.0701403185 -1.068017661 -1.03776918 -0.9911465523  0.915120526
## 22  -0.9735154847 -1.031776926 -0.97117149 -1.0548079120  0.283657632
## 23  -1.0431755856 -1.101993004 -1.03551207 -1.0218401907  0.338120106
## 24  -0.9825040660 -1.062354689 -0.97342871 -0.9854621409  0.225353975
## 25  -1.0341870043 -1.031776926 -1.00390628 -0.9865991824  0.422900481
## 26  -0.9487973916 -1.009125830 -0.93053522 -0.9479470497  0.963135302
## 27  -0.9611563820 -1.021583735 -0.94859618 -0.9422635479 -0.380318126
## 28  -0.9353154184 -0.995536078 -0.90570189 -1.0093351224 -1.052662172
## 29  -0.9139677343 -0.902668904 -0.84361944 -0.8501810409 -0.053817650
## 30  -0.7959961153 -0.826789062 -0.73864258 -0.7740147266 -0.945246260
## 31  -0.8027370457 -0.841512359 -0.75331653 -0.8376759726 -0.319682323
## 32  -0.7476835861 -0.764500557 -0.71719550 -0.6978484122  0.166501579
## 33  -0.6959998613 -0.727126955 -0.63818108 -0.6842063025 -0.337790753
## 34  -0.6589234520 -0.670500630 -0.60431728 -0.5909875499 -0.714089410
## 35  -0.6016224537 -0.627464962 -0.53320434 -0.5762093082 -0.173580219
## 36  -0.5297157130 -0.539127215 -0.44628755 -0.4943585825 -0.092503841
## 37  -0.4330907669 -0.525537463 -0.44290142 -0.5398312584 -0.542745253
## 38  -0.5622986182 -0.578766322 -0.50159765 -0.5580198286  0.269390384
## 39  -0.5780281299 -0.675030849 -0.61899111 -0.6614703624  0.224942420
## 40  -0.6488111014 -0.746379680 -0.66978687 -0.7319522655  0.685061157
## 41  -0.6656644947 -0.727126955 -0.69236217 -0.7353633897  0.540605275
## 42  -0.6802702372 -0.742982327 -0.75557364 -0.7683303152  0.760101393
## 43  -0.7353245957 -0.774692842 -0.67994546 -0.7330893069  0.573255322
## 44  -0.7319531755 -0.807536224 -0.69349118 -0.7933403382  0.936109843
## 45  -0.8049837978 -0.878885168 -0.83910420 -0.8092556213  0.398481538
## 46  -0.8622847961 -0.863029797 -0.80749841 -0.7876567226  0.063064033
## 47  -0.7622894409 -0.766765270 -0.70026354 -0.6921638872 -0.015680199
## 48  -0.6667882640 -0.756572871 -0.61786300 -0.6819330154  0.616468621
## 49  -0.6274644285 -0.677295562 -0.58061296 -0.6250923126  1.601869006
## 50  -0.6342053589 -0.709006191 -0.59302968 -0.6580600339  0.795769512
## 51  -0.6420701709 -0.694283573 -0.65172590 -0.7490045900  1.647826005
## 52  -0.7409417568 -0.737319354 -0.66188539 -0.6626064944 -0.036395146
## 53  -0.6083634965 -0.679561069 -0.62350635 -0.6978484122 -0.606947868
## 54  -0.7016179213 -0.744115080 -0.66978687 -0.7387737182 -0.686240840
## 55  -0.7690303713 -0.754307365 -0.67994546 -0.6876166310 -0.831108278
## 56  -0.7173474330 -0.781488681 -0.73412745 -0.8194874025  0.378178147
## 57  -0.7566713809 -0.812066444 -0.70590790 -0.7774249414 -0.879260239
## 58  -0.7252122451 -0.774692842 -0.69913453 -0.7751517680 -0.797772305
## 59  -0.7521770903 -0.784886034 -0.67543022 -0.7296789784 -1.085449405
## 60  -0.7016179213 -0.694283573 -0.63705207 -0.6785226869 -0.895859633
## 61  -0.6049929751 -0.684091288 -0.60093104 -0.6933010423  0.696584704
## 62  -0.6027462230 -0.651247906 -0.56593913 -0.5898505085  1.176046537
## 63  -0.5701634302 -0.667103277 -0.58174197 -0.6614703624  4.043214579
## 64  -0.6184758471 -0.684091288 -0.60093104 -0.6717012342  1.122681543
## 65  -0.6656644947 -0.631994276 -0.60318827 -0.5932616327  2.363108980
## 66  -0.5454452248 -0.629729676 -0.51062813 -0.5932616327 -0.810530517
## 67  -0.5993758140 -0.668236030 -0.54900718 -0.6023555768 -1.008900134
## 68  -0.6353291282 -0.704476765 -0.60996153 -0.6637435358 -0.646182799
## 69  -0.6297110682 -0.688620601 -0.58512821 -0.6205449427 -0.972957644
## 70  -0.5847691727 -0.591224114 -0.53433244 -0.5148212355 -0.571279748
## 71  -0.4690441934 -0.482500890 -0.40113614 -0.4250128114 -0.663605303
## 72  -0.4027555127 -0.468911138 -0.35598553 -0.4409280945 -0.920278576
## 73  -0.4117431952 -0.389633716 -0.38871943 -0.3192891043 -0.234353207
## 74  -0.2825353439 -0.314887419 -0.25665214 -0.3545299989 -0.833028869
## 75  -0.2746706442 -0.344333335 -0.24084930 -0.3181520628  0.741992963
## 76  -0.3477011542 -0.415682165 -0.36953036 -0.3533938670 -1.201645162
## 77  -0.3555659662 -0.381706030 -0.32212173 -0.3477094557 -0.639323545
## 78  -0.3477011542 -0.371513745 -0.29954643 -0.3136046929 -0.620529190
## 79  -0.3791602900 -0.474573204 -0.38307608 -0.4636640346 -0.683359954
## 80  -0.4207317765 -0.448525547 -0.36501512 -0.3727194786  0.332221148
## 81  -0.3566897355 -0.408887346 -0.29728841 -0.3317940589  0.033706427
## 82  -0.2443352776 -0.225417712 -0.39662181 -0.2920057943  2.635009796
## 83  -0.2668058321 -0.306959734 -0.28487169 -0.3852245469  0.577233689
## 84  -0.3701726075 -0.441730728 -0.38307608 -0.4602538198  0.459528896
## 85  -0.3937669313 -0.395296688 -0.33228032 -0.3249735156 -0.108280124
## 86  -0.4690441934 -0.509682092 -0.49934053 -0.5466519154  0.599320486
## 87  -0.6195987176 -0.663705811 -0.59641580 -0.6125873580  0.566807624
## 88  -0.6016224537 -0.635392648 -0.55803676 -0.6137243994 -0.406794845
## 89  -0.6577996827 -0.658042838 -0.66188539 -0.5943978784 -0.028026856
## 90  -0.6083634965 -0.600283760 -0.63253683 -0.5375571756 -0.018149531
## 91  -0.7072359812 -0.766765270 -0.71493827 -0.7501417451  0.427976329
## 92  -0.7229654930 -0.680693029 -0.65849916 -0.6285026411 -0.311588404
## 93  -0.6915063571 -0.626332209 -0.63027961 -0.5762093082 -0.411047582
## 94  -0.5285919438 -0.521007244 -0.44177242 -0.4943585825 -1.027557304
## 95  -0.5263451917 -0.548187540 -0.50272665 -0.5682516098 -0.423531424
## 96  -0.6072405137 -0.557247979 -0.56932525 -0.5591568700 -0.381552791
## 97  -0.4870213561 -0.537995255 -0.47450710 -0.4648010760 -0.838241902
## 98  -0.4757852362 -0.559512692 -0.58625631 -0.6887536724 -0.352332371
## 99  -0.7510541075 -0.804138871 -0.75896077 -0.8160770740  0.730195047
## 100 -0.7521770903 -0.834716634 -0.71493827 -0.7592363712 -0.917809245
## 101 -0.7263360144 -0.758837584 -0.70703691 -0.8115298178 -0.087016438
## 102 -0.8544199840 -0.838115006 -0.79282368 -0.7649199868 -0.380180941
## 103 -0.7364474662 -0.748645299 -0.64721066 -0.7046689555 -1.219342037
## 104 -0.8634085654 -0.898138685 -0.85490703 -0.9570417895 -0.044351880
## 105 -0.9207087771 -0.934379533 -0.84813468 -0.9104320722 -0.051073949
## 106 -0.8330724123 -0.718066517 -0.76121789 -0.6387335129 -0.014582719
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## 318 -1.0914880025 -1.104258510 -1.20257172 -1.0570811991  1.116645400
## 319 -0.8948677012 -0.829054568 -0.92827810 -0.9900095108  0.445947574
## 320 -0.9454268702 -0.915125903 -0.94633817 -0.8842858037  0.635811716
## 321 -0.8038608150 -0.774692842 -0.84361944 -0.7922040925  0.243599590
## 322 -0.8263313695 -0.917391522 -0.90908813 -0.9445368349  0.582035167
## 323 -0.7948723460 -0.848307292 -0.85264902 -0.8444974254  0.445536019
## 324 -0.9521686995 -1.052162403 -0.97681574 -1.0070619490  1.266863055
## 325 -0.9993573471 -0.842644320 -1.01293677 -0.9149794421  0.919647634
## 326 -0.7218417237 -0.681825782 -0.73864258 -0.7490045900  2.531572251
## 327 -0.8117256271 -0.735054641 -0.86280851 -0.8001617909  0.784931891
## 328 -0.6252167775 -0.679561069 -0.95875477 -0.9684098164  1.616273438
## 329 -0.8207133096 -0.884548140 -0.92827810 -0.9206638534  0.168147799
## 330 -0.9094734437 -0.848307292 -0.84136131 -0.9468100083  0.043172197
## 331 -0.8454314027 -0.825657101 -0.83007371 -0.7683303152 -0.371675466
## 332 -0.7274597837 -0.660307438 -0.66414262 -0.6171346142  0.866419825
## 333 -0.4375850576 -0.455320480 -0.48240858 -0.5773454402  0.868477601
## 334 -0.4297203579 -0.515344272 -0.49708331 -0.5284633452  0.394640356
## 335 -0.5926338724 -0.624066703 -0.58738532 -0.5693878555  0.206422435
## 336 -0.6825178881 -0.710138945 -0.70026354 -0.6762486041  0.383253995
## 337 -0.6937531092 -0.739584860 -0.71268025 -0.6671538643 -0.111161011
## 338 -0.6330815896 -0.643320220 -0.59980204 -0.7023956684 -0.345610302
## 339 -0.6139815565 -0.684091288 -0.60431728 -0.6751115627 -0.213363891
## 340 -0.5858929420 -0.646717686 -0.57609772 -0.6398705543 -0.476896418
## 341 -0.5555567889 -0.588958608 -0.51627238 -0.5250521073 -0.308021592
## 342 -0.5589280968 -0.639922867 -0.56142389 -0.6148605314 -0.586232921
## 343 -0.7656598499 -0.696549193 -0.73751357 -0.8410872106  0.643905635
## 344 -0.8757676681 -0.891342960 -0.86732375 -0.9365791365  2.888253443
## 345 -1.0476698763 -0.930982067 -1.02761060 -0.8672333655  0.551168525
## 346 -0.8791381895 -0.836982253 -0.83458896 -0.8422233426  2.607572782
## 347 -0.5521862676 -0.549320407 -0.53207533 -0.4852638427  0.599457671
## 348 -0.5005033293 -0.537995255 -0.54900718 -0.6466912113 -0.080980295
## 349 -0.6151053258 -0.588958608 -0.53433244 -0.5921245913  0.203129993
## 350 -0.5791518992 -0.654645372 -0.60093104 -0.6876166310 -0.738508353
## 351 -0.7061122119 -0.732789928 -0.65962738 -0.6614703624 -0.549604507
## 352 -0.4769090055 -0.253730874 -0.43274272 -0.1931026304  0.716339354
## 353 -0.0117615498  0.013545495 -0.02186522  0.0933741706  0.046601824
## 354  0.1107052588  0.059978629  0.00861145 -0.0259908505  0.191332077
## 355 -0.0522092670 -0.075924212 -0.07266098 -0.0430431750  0.808116169
## 356 -0.0140083019 -0.056671488 -0.04444143 -0.1271671878 -0.846061451
## 357 -0.1578226821 -0.167658633 -0.06701673 -0.0919262931 -0.069593933
## 358 -0.0387263950 -0.004575382  0.05037673  0.0444910525 -0.464961316
## 359  0.0107090047  0.082629612  0.06505056  0.1649939109 -0.681439363
## 360  0.3230550718  0.408796907  0.35176135  0.4219133191  0.965879004
## 361  0.4050733767  0.493736395  0.46915481  0.5219535244 -0.043117214
## 362  0.4208028884  0.508459806  0.46689679  0.5355947246 -0.553308504
## 363  0.4893392201  0.414459993  0.38788238  0.2911803847  0.212458578
## 364  0.1365462223  0.045256011 -0.09410807 -0.1885552605  0.357051646
## 365  0.0466623189 -0.043080830 -0.22617547 -0.1214836859 -0.084821477
## 366 -0.3151182490 -0.123490099 -0.27809934 -0.0589585718 -0.638911990
## 367  0.1983415112  0.169833814  0.04247525  0.0308498523 -0.087839548
## 368  0.0848632840  0.022605820  0.06956580  0.0069768708 -1.105341241
## 369  0.0511566096  0.018075714  0.06279345  0.0263024824 -1.331422242
## 370  0.1365462223  0.079232146  0.05940631  0.0820061437  1.209931250
## 371  0.0140803126  0.002220343  0.01425570  0.0547228338 -1.165977043
## 372  0.1477815557  0.092822011  0.13954973  0.0604064493 -0.824386210
## 373 -0.0274911739 -0.120092746 -0.13135732 -0.1953767132 -0.781310096
## 374 -0.2252352445 -0.185779510 -0.19682680 -0.1123889461 -0.691453873
## 375 -0.1533283914 -0.185779510 -0.14038769 -0.2044706573  0.521399364
## 376 -0.0758035908 -0.057803449 -0.15167539 -0.0635059417 -0.605027277
## 377 -0.1274865291  0.028268000 -0.06363050  0.0695010754  0.280090820
## 378  0.1556463678  0.072436420  0.07295203  0.0012924595 -0.620529190
## 379  0.1365462223  0.238917931  0.15761070  0.1638568694 -0.451654364
## 380  0.3163132426  0.274026932  0.33821562  0.3321058044  0.238249372
## 381  0.2590130308  0.230990358  0.25355696  0.1740878549 -0.680341882
## 382  0.1792406916  0.165303594  0.18357303  0.1388468465 -0.858133738
## 383  0.1062109681  0.078099393  0.09214111  0.0217551125 -0.580196778
## 384  0.0208213554  0.057713916  0.10568683  0.1138368236 -0.557012501
## 385  0.1387938732  0.189087444  0.13277738  0.1593094995  0.141122340
## 386  0.1253110012  0.247978369  0.16776929  0.3196007361  0.446770684
## 387  0.4354095297  0.454098194  0.44319248  0.5094484561  0.006680968
## 388  0.4466448632  0.517519452  0.52785114  0.5924354274 -0.056835722
## 389  0.6241650190  0.565086132  0.63621412  0.5901621403  2.504958346
## 390  0.5421459277  0.544700654  0.58203303  0.6185824917  0.655703552
## 391  0.5556287997  1.011301010  0.62718454  1.0710349407  2.576294585
## 392  1.0848181840  1.292168037  1.16448418  1.2517879208  1.440264989
## 393  1.2533498709  1.302360323  1.31461244  1.3438696320  1.129129241
## 394  1.3848043619  1.387299811  1.32815816  1.3074915822  1.188667563
## 395  1.2994147492  1.347661497  1.34508922  1.3279542352 -0.309256257
## 396  1.3252566116  1.318215581  1.31574145  1.2745238609  0.253614100
## 397  1.2230137178  1.269516941  1.31574145  1.3359119336 -0.231060765
## 398  1.2308784175  1.148336604  1.20512045  1.1790319349 -0.787346240
## 399  1.1297600795  1.143806385  1.16787041  1.2324623092 -0.308707517
## 400  1.2129021537  1.267252228  1.28639288  1.2927133405 -0.690219207
## 401  1.1836897699  1.167590235  1.25817333  1.2131366976 -0.544528659
## 402  1.2836851251  1.356721935  1.31461244  1.4393624674 -0.472369310
## 403  1.3881748833  1.404287822  1.48731600  1.4916558003 -0.517640385
## 404  1.4477226336  1.544720883  1.51892179  1.6326204021 -0.618059859
## 405  1.6185018590  1.772358938  1.58100515  1.7747221591  0.618252027
## 406  1.8319754414  1.847106140  1.85755644  1.8361102318 -0.397054705
## 407  1.8465812962  1.811997252  1.86432970  1.8292896885 -0.324483801
## 408  1.7802917166  1.702142068  1.68146665  1.6212524889 -0.359603180
## 409  1.6095132777  1.638720810  1.65437610  1.6724087804 -0.612709641
## 410  1.6937791211  1.646648495  1.69501237  1.6337575572  0.061006257
## 411  1.6611962160  1.640986316  1.69614048  1.6280731459 -1.132778255
## 412  1.6050189870  1.592287676  1.62615655  1.5769168544 -0.562637089
## 413  1.5162588529  1.513010254  1.52795216  1.4711930336 -1.135659142
## 414  1.4454758815  1.409950794  1.47941452  1.4530036676 -0.702154309
## 415  1.4690702053  1.463179767  1.50650507  1.4393624674 -0.159998897
## 416  1.4926645291  1.455251175  1.44667973  1.3836588060 -0.668955521
## 417  1.3364919450  1.387299811  1.36202106  1.4359521389 -0.972820459
## 418  1.4904177770  1.422408699  1.42410442  1.3745640662 -0.969253647
## 419  1.3387385847  1.303493076  1.37330877  1.3927526364 -0.908617845
## 420  1.3252566116  1.322745800  1.39249863  1.3188594954 -0.516680089
## 421  1.3263803809  1.374841906  1.31574145  1.4564147919 -0.112258491
## 422  1.4454758815  1.496022243  1.54488412  1.5507696764 -0.380729681
## 423  1.6499609949  1.590022170  1.29090812  1.2745238609 -0.276606210
## 424  1.3724452592  1.334070952  1.31574145  1.2938503819 -0.441502669
## 425  1.1960488726  1.160794509  1.12949227  1.0517084196 -0.081940590
## 426  1.0904353451  1.191373178  1.15093936  1.2188201995  0.434972768
## 427  1.1645897368  1.204962930  1.01661405  0.9778152786 -0.543842734
## 428  0.9971818192  1.046409898  1.07756840  1.0971810955  1.413925455
## 429  1.1758249579  1.313685362  1.22430952  1.3779743947 -0.357682588
## 430  1.3724452592  1.357854688  1.38911251  1.3109019107 -0.094973172
## 431  1.4151397285  1.392962783  1.41055870  1.3847949380 -0.663193748
## 432  1.2207669657  1.348794363  1.22769655  1.3211336919 -0.261378667
## 433  1.3218860902  1.274047160  1.19947699  1.1369694738  0.582858277
## 434  1.0376295364  0.972795562  0.79085762  0.8664080696 -0.200880049
## 435  0.8207853203  0.926361522  0.85971333  0.9209745759 -0.644399393
## 436  0.9443751117  0.969398095  0.88228863  0.8379877182 -0.475387382
## 437  0.7949434579  0.929758988  0.79650187  0.9005128323 -0.985578671
## 438  0.9398808210  1.031686487  0.95453069  1.0562557895 -0.849628263
## 439  1.1791954792  1.320481200  1.27736240  1.3995740891 -0.307198481
## 440  1.3578395167  1.382769592  1.40265733  1.3541014132 -0.949636182
## 441  1.3881748833  1.439395917  1.27849141  1.2551982493  0.236465966
## 442  1.3297509022  1.271782447  1.15206758  1.1653898252 -0.927686570
## 443  1.0533590482  1.228745873  1.07192404  1.2222305280 -1.060893277
## 444  1.3207623209  1.522070692  1.39136963  1.5746426579 -0.845512711
## 445  1.5623246301  1.682889230  1.59116373  1.5280337364 -0.766905664
## 446  1.6398486443  1.699877354  1.69501237  1.7792695290 -1.031810041
## 447  1.7342268383  1.695347135  1.65324709  1.7849539403 -1.416751358
## 448  1.7780449646  1.808599786  1.74693624  1.7031032146  0.034803908
## 449  1.8027630576  1.730455230  1.78079914  1.7667645744 -0.090446065
## 450  1.6589494639  1.688552316  1.60809558  1.6257998588 -0.199096643
## 451  1.5825484325  1.699877354  1.68259566  1.6746828632 -0.863483956
## 452  1.5510892967  1.613805113  1.63744426  1.6962826713 -1.213031524
## 453  1.7342268383  1.850503607  1.64760284  1.7849539403  0.145375077
## 454  1.7746744432  1.881081369  1.87561730  1.9122766598  1.075489878
## 455  1.9330937794  1.926382656  1.76612531  1.8304258205  2.903755356
## 456  1.7881564163  1.901466960  1.88126076  1.8952251311  0.996471275
## 457  2.0050006324  1.980744268  1.94673024  1.9838964001 -0.112670047
## 458  2.0106186924  2.035105088  2.07541140  2.0918933943 -0.350960520
## 459  1.9196118062  1.860695892  1.82143541  1.8565729985  0.116017471
## 460  1.8634345773  1.857298426  1.69501237  1.6155680776 -0.648377760
## 461  1.4173872671  1.419010440  1.30219572  1.2108625011  0.058811296
## 462  1.1769488395  1.312553515  1.22656754  1.2995338838 -0.755244932
## 463  1.2218907350  1.233276093  1.24349950  1.2665661625 -0.623410076
## 464  1.4016577553  1.576432418  1.47038404  1.6587675801 -0.430116307
## 465  1.7881564163  1.963756257  1.82595065  1.9941273855 -0.235039133
## 466  2.0836492024  2.073611442  1.72210292  1.6496728403 -0.398975296
## 467  1.6881610612  1.848238100  1.66340568  1.8611203684 -0.814508884
## 468  1.9611823939  1.884478836  1.84175349  1.8020054691 -0.934271454
## 469  2.3398172540  2.359007783  1.92754116  1.8599832133  0.880001147
## 470  1.7656858618  1.995466886  1.84401151  1.9190981126  0.291614366
## 471  1.9735414966  1.913924751  1.83611015  1.8111002089 -0.435054970
## 472  1.7499563501  1.716865478  1.70065572  1.6769561503 -0.671424852
## 473  1.7589449314  1.720262831  1.76951154  1.7815437255 -0.742349535
## 474  1.8454574146  1.842575921  1.65324709  1.7826798575 -0.584861071
## 475  1.7095086329  1.630793124  1.65663333  1.6576305387 -1.017542793
## 476  1.7151266928  1.807467033  1.69726949  1.7735851177 -1.068987196
## 477  1.6971496425  1.725925804  1.65550432  1.7963219672 -1.083803184
## 478  1.5701894421  1.677227164  1.62615655  1.6530831688 -0.208425228
## 479  1.8600640559  1.782552129  1.70968620  1.7417545515 -0.916437394
## 480  1.6465904735  1.792744528  1.74693624  1.8383844283  0.227548936
## 481  1.8443336453  1.787081556  1.71533045  1.6576305387 -0.696255351
## 482  1.6488371133  1.663636506  1.60470946  1.5348543933 -1.676168333
## 483  1.4679464360  1.462047014  1.46925594  1.4575510375  0.385311771
## 484  1.5477179888  1.573034045  1.56971744  1.4825610604 -0.538080961
## 485  1.4409815909  1.473372052  1.48618699  1.4677828188 -0.756479598
## 486  1.4510930426  1.422408699  1.43652114  1.3654693264  0.354033574
## 487  1.3657043288  1.404287822  1.39362685  1.4166256178  0.316170494
## 488  1.3746929101  1.338601171  1.39927110  1.3722899834 -0.329696833
## 489  1.3466042956  1.344264144  1.43990737  1.3836588060 -1.271746735
## 490  1.3746929101  1.426938012  1.43990737  1.4939289736 -0.129818181
## 491  1.4117692071  1.375974659  1.37218055  1.3086286236 -1.065146014
## 492  1.2072840937  1.238939065  1.27849141  1.2756609023 -0.933448343
## 493  1.2915499371  1.295565390  1.24462760  1.1619794967 -0.991614815
## 494  1.3466042956  1.419010440  1.38685438  1.4836981019 -0.115688118
## 495  1.4319930095  1.425806165  1.49521736  1.4382253123 -0.548095471
## 496  1.3825576098  1.345396897  1.33267340  1.3109019107 -0.148063796
## 497  1.3589624995  1.311420762  1.31461244  1.3381860164  2.614020480
## 498  1.1825660006  1.267252228  1.20963569  1.3199965368 -0.852509150
## 499  1.2937974757  1.212890615  1.27623429  1.2085893277 -1.228121882
## 500  1.2185194272  1.190240425  1.27849141  1.2335984412 -1.180930216
## 501  1.2612146829  1.269516941  1.31461244  1.3495540433 -1.974408683
## 502  1.4117692071  1.357854688  1.35750582  1.2893030120 -1.681106995
## 503  1.3297509022  1.292168037  1.23559802  1.2597456192 -1.318801216
## 504  1.2814384853  1.255927189  1.33380151  1.3222707333 -1.715677634
##        adjusted      closem1
## 1   -1.83672403 -1.852503023
## 2   -1.96602462 -1.852503023
## 3   -1.72382807 -2.004579702
## 4   -1.73058354 -1.719719981
## 5   -1.67558100 -1.727664279
## 6   -1.76242523 -1.662974994
## 7   -1.71128372 -1.765115744
## 8   -1.68426603 -1.704966171
## 9   -1.63312420 -1.673189092
## 10  -1.67172136 -1.613039179
## 11  -1.62444068 -1.658435282
## 12  -1.50093014 -1.602825081
## 13  -1.34268144 -1.457558028
## 14  -1.43241967 -1.271434471
## 15  -1.47391072 -1.376980260
## 16  -1.45750771 -1.425780949
## 17  -1.37066434 -1.406487426
## 18  -1.50478968 -1.304346677
## 19  -1.16320498 -1.462097741
## 20  -1.17188969 -1.060343574
## 21  -1.09759056 -1.070557217
## 22  -1.15162552 -0.983170278
## 23  -1.12364219 -1.046724436
## 24  -1.09276496 -1.013812230
## 25  -1.09373059 -0.977495439
## 26  -1.06092166 -0.978630566
## 27  -1.05609821 -0.940043521
## 28  -1.08227764 -0.934369589
## 29  -0.94652901 -1.001328220
## 30  -0.88156363 -0.842442143
## 31  -0.93586379 -0.766404088
## 32  -0.81659910 -0.829958132
## 33  -0.80496285 -0.690366032
## 34  -0.72545342 -0.676746895
## 35  -0.71284830 -0.583685116
## 36  -0.64303350 -0.568931760
## 37  -0.68181977 -0.487218865
## 38  -0.69733398 -0.532614969
## 39  -0.78556971 -0.550772910
## 40  -0.84568655 -0.654049240
## 41  -0.84859693 -0.724412457
## 42  -0.87671583 -0.727817837
## 43  -0.84665585 -0.760729248
## 44  -0.89804681 -0.725547584
## 45  -0.91162285 -0.785697156
## 46  -0.89319945 -0.801585639
## 47  -0.81174969 -0.780023111
## 48  -0.80302425 -0.684691079
## 49  -0.75454291 -0.674477436
## 50  -0.78266095 -0.617732449
## 51  -0.86023189 -0.650644655
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## 
## $exclude
## NULL
## 
## $net.result
## $net.result[[1]]
##                [,1]
##   [1,] -1.758252711
##   [2,] -1.682053951
##   [3,] -1.718795868
##   [4,] -1.640106860
##   [5,] -1.563304492
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##  [13,] -1.405726791
##  [14,] -1.353917825
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## 
## $weights
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## $weights[[1]][[1]]
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## [171,] -2.263171e-01  1.909007e-01 -7.003289e-01  7.512031e-03 -2.996152e-02
## [172,] -3.894181e-01  3.146235e-01 -1.195507e+00 -6.952361e-03 -5.027994e-02
## [173,] -4.523345e-01  3.163164e-01 -1.180149e+00  5.192173e-03 -3.616095e-02
## [174,] -9.608673e-01  4.968334e-01 -2.101416e+00  5.898447e-03 -4.119785e-02
## [175,] -1.223296e+00  6.564510e-01 -2.782511e+00 -1.879299e-02 -6.335206e-02
## [176,] -1.228780e+00  8.640644e-01 -3.562354e+00 -8.417459e-02 -1.401724e-01
## [177,] -6.614992e-01  6.569992e-01 -2.520078e+00 -1.002766e-01 -1.307499e-01
## [178,] -7.320021e-01  6.886837e-01 -2.737584e+00 -1.117284e-01 -1.410701e-01
## [179,] -6.968976e-01  5.570176e-01 -2.184330e+00 -5.222114e-02 -9.291319e-02
## [180,] -5.803621e-01  5.369913e-01 -2.087128e+00 -7.058635e-02 -1.030894e-01
## [181,] -5.048093e-01  6.426158e-01 -2.431007e+00 -1.066055e-01 -1.511793e-01
## [182,] -9.682023e-01  5.534628e-01 -2.293519e+00  3.242585e-03 -5.970209e-02
## [183,] -4.705237e-01  3.852272e-01 -1.441826e+00 -2.191278e-02 -5.836299e-02
## [184,] -4.881384e-01  3.869037e-01 -1.383064e+00  1.267853e-02 -5.065843e-02
## [185,] -4.092436e-01  2.642643e-01 -1.064340e+00  2.052990e-02 -3.608877e-02
## [186,] -3.478132e-01  2.695957e-01 -9.989979e-01 -3.766134e-03 -3.687288e-02
## [187,] -3.754500e-01  3.122962e-01 -1.181582e+00 -1.380755e-02 -5.063569e-02
## [188,] -5.168511e-01  3.948924e-01 -1.502551e+00 -1.642150e-02 -5.681814e-02
## [189,] -2.974951e-01  2.301148e-01 -9.000579e-01  3.938502e-03 -3.866879e-02
## [190,] -2.370200e-01  1.541129e-01 -6.241199e-01  9.765997e-03 -2.191181e-02
## [191,] -2.073711e-01  1.315629e-01 -5.141218e-01  7.840435e-03 -1.501447e-02
## [192,] -2.603962e-01  1.964674e-01 -7.397956e-01 -1.246541e-03 -2.724098e-02
## [193,] -2.903171e-01  2.135788e-01 -8.176663e-01  3.241377e-03 -3.048240e-02
## [194,] -2.493844e-01  1.782067e-01 -6.949486e-01  5.105997e-03 -2.605002e-02
## [195,] -2.348478e-01  1.692320e-01 -6.718455e-01  1.650648e-03 -2.637912e-02
## [196,] -2.539709e-01  1.957627e-01 -7.726124e-01 -3.211979e-03 -3.290882e-02
## [197,] -5.304906e-01  3.341970e-01 -1.315070e+00  1.705277e-02 -3.826531e-02
## [198,] -4.141428e-01  3.167595e-01 -1.184203e+00 -6.439585e-03 -4.341132e-02
## [199,] -4.599547e-01  3.480790e-01 -1.294173e+00 -9.493195e-03 -4.547836e-02
## [200,] -6.935055e-01  5.208948e-01 -2.042907e+00 -3.029485e-02 -8.040531e-02
## [201,] -8.481528e-01  5.613648e-01 -2.288166e+00 -3.251636e-02 -7.813842e-02
## [202,] -1.098129e+00  5.081410e-01 -2.251551e+00  1.979195e-02 -3.526514e-02
## [203,] -5.802482e+00  6.724556e-01 -7.575886e+00  1.888620e-01  1.301978e-01
## [204,] -9.371160e+00 -2.162586e+00 -9.534791e+00 -7.567385e-03  7.492225e-02
## [205,]  3.550182e+00  6.939721e-02  4.208310e+00  1.377121e-01 -7.140188e-02
## [206,]  2.773429e+00 -5.950909e-01  5.429927e+00  3.952652e-01  1.274630e-01
## [207,]  2.959895e+00 -5.471922e-04  3.893674e+00  2.638286e-01 -2.074929e-02
## [208,]  1.388013e+00  2.175123e-01  2.090327e+00  3.514507e-01  4.449719e-02
## [209,]  1.516141e+00  1.982101e-01  2.252968e+00  3.355897e-01  4.519631e-02
## [210,]  1.736623e+00  7.048289e-02  2.634664e+00  3.037956e-01  3.304039e-02
## [211,]  2.419071e+00  1.152979e-02  3.406732e+00  2.558973e-01  1.486583e-02
## [212,]  2.396417e+00  4.624436e-01  2.398421e+00  2.391569e-01 -4.756196e-02
## [213,]  1.569666e+00  7.337303e-01  1.535661e+00  3.957040e-01  2.510228e-02
## [214,]  1.484572e+00  9.403907e-01  1.486115e+00  5.034091e-01  7.543407e-02
## [215,]  1.051725e+00  6.191498e-01  1.622463e+00  5.209934e-01  1.114194e-01
## [216,]  1.358377e+00  1.124050e+00  1.331807e+00  6.005721e-01  9.936748e-02
## [217,]  1.135820e+00  7.395374e-01  1.521385e+00  5.303256e-01  9.735176e-02
## [218,]  9.094668e-01  6.265914e-01  1.556019e+00  5.650972e-01  1.220274e-01
## [219,]  9.051636e-01  6.694274e-01  1.498302e+00  5.683878e-01  1.240031e-01
## [220,]  1.071924e+00  5.357916e-01  1.640836e+00  4.767567e-01  9.408307e-02
## [221,]  8.268360e-01  6.761355e-01  1.559382e+00  6.068266e-01  1.521728e-01
## [222,]  1.094221e+00  9.674242e-01  1.409778e+00  6.221898e-01  1.263375e-01
## [223,]  1.092979e+00  7.535021e-01  1.511604e+00  5.476555e-01  1.063556e-01
## [224,]  8.263728e-01  5.847447e-01  1.645411e+00  5.844398e-01  1.519887e-01
## [225,]  8.880048e-01  3.856945e-01  1.752394e+00  4.998967e-01  1.195642e-01
## [226,]  1.359441e+00  8.728902e-01  1.418572e+00  4.954680e-01  6.672082e-02
## [227,]  1.056604e+00  8.803785e-01  1.454757e+00  6.062217e-01  1.231801e-01
## [228,]  1.597448e+00  1.661410e+00  1.480103e+00  8.014039e-01  1.663206e-01
## [229,]  1.668018e+00  2.264070e+00  1.472111e+00  1.056905e+00  2.432748e-01
## [230,]  1.551352e+00  2.285537e+00  1.645930e+00  1.129959e+00  2.956874e-01
## [231,]  9.584878e-01  1.549068e+00  1.544423e+00  9.781641e-01  2.627862e-01
## [232,]  1.129787e+00  1.733774e+00  1.567245e+00  1.020341e+00  2.777883e-01
## [233,]  1.424940e+00  9.784811e-01  1.431300e+00  5.210075e-01  7.753322e-02
## [234,]  1.009046e+00  9.701887e-01  1.467461e+00  6.708600e-01  1.569672e-01
## [235,]  1.292466e+00  1.051894e+00  1.329090e+00  5.785621e-01  9.312511e-02
## [236,]  1.008934e+00  1.328112e+00  1.445585e+00  8.354330e-01  2.016889e-01
## [237,]  9.654490e-01  1.191317e+00  1.447678e+00  7.771604e-01  1.875365e-01
## [238,]  1.386954e+00  1.627470e+00  1.376326e+00  8.280985e-01  1.769027e-01
## [239,]  1.214009e+00  1.753530e+00  1.476192e+00  9.690481e-01  2.415236e-01
## [240,]  4.320992e+00  4.894431e+00  2.459146e+00  1.872150e+00  3.414519e-01
## [241,]  2.530589e+00  5.051696e+00  2.715630e+00  2.489494e+00  7.326350e-01
## [242,] -2.390132e+02 -3.937712e+02 -1.068648e+02 -1.494477e+02 -3.461939e+01
## [243,]  1.677669e+01  2.474162e+01  7.801864e+00  9.794549e+00  2.003930e+00
## [244,]  4.392462e+00  6.309315e+00  2.324323e+00  2.420122e+00  5.204113e-01
## [245,]  1.635949e+00  1.418063e+00  1.294454e+00  5.994124e-01  9.443886e-02
## [246,]  2.812916e+00  1.344207e+00  9.129680e-01  3.131307e-01 -2.016706e-01
## [247,]  1.337363e+00  1.041993e+00  1.354087e+00  5.326268e-01  8.786216e-02
## [248,]  9.433690e-01  5.886712e-01  1.567058e+00  5.059826e-01  1.044207e-01
## [249,]  1.032098e+00  6.479027e-01  1.562277e+00  4.987324e-01  1.004850e-01
## [250,]  1.071251e+00  7.475175e-01  1.458410e+00  5.094369e-01  9.515521e-02
## [251,]  1.116843e+00  6.344377e-01  1.563938e+00  4.658859e-01  8.544153e-02
## [252,]  1.356527e+00  7.812086e-01  1.451204e+00  4.329490e-01  5.022175e-02
## [253,]  1.885649e+00  1.105005e+00  1.283617e+00  3.752659e-01 -1.203571e-02
## [254,]  1.870472e+00  8.159500e-01  1.553126e+00  3.107597e-01 -2.097688e-02
## [255,]  1.941144e+00  8.153575e-01  1.564935e+00  2.869696e-01 -3.256922e-02
## [256,]  1.930965e+00  7.112598e-01  1.653083e+00  2.675246e-01 -3.898362e-02
## [257,]  2.138167e+00  8.925069e-01  1.664078e+00  2.903113e-01 -3.381851e-02
## [258,]  1.761230e+00  8.438984e-01  1.535342e+00  3.410169e-01  8.926518e-03
## [259,]  1.280203e+00  5.188340e-01  1.700342e+00  3.874658e-01  5.941443e-02
## [260,]  1.514918e+00  5.004383e-01  1.802032e+00  3.418848e-01  2.007305e-02
## [261,]  1.466704e+00  7.071745e-01  1.513922e+00  3.724168e-01  2.988485e-02
## [262,]  1.592095e+00  5.341441e-01  1.757931e+00  3.134604e-01  9.062122e-03
## [263,]  1.806617e+00  6.709922e-01  1.724373e+00  3.116507e-01 -1.185623e-02
## [264,]  1.701214e+00  8.851016e-01  1.392663e+00  3.532966e-01  1.359432e-03
## [265,]  1.446451e+00  5.498438e-01  1.737385e+00  3.497626e-01  4.220703e-02
## [266,]  1.216556e+00  2.165147e-01  2.174403e+00  3.823614e-01  8.713584e-02
## [267,]  2.319393e+00  2.587587e-01  3.040076e+00  3.081638e-01  1.495903e-02
## [268,]  1.844227e+00  3.751141e-01  2.245116e+00  2.791351e-01  4.583617e-03
## [269,]  4.180913e+00  4.766613e-01  4.106031e+00  7.899561e-02 -1.650733e-01
## [270,]  4.378885e+00  1.208616e+00  3.254553e+00  7.438027e-02 -1.743811e-01
## [271,]  2.196331e+00  5.218414e-01  2.214145e+00  2.107094e-01 -1.967009e-02
## [272,]  4.087329e+00  4.946004e-01  4.048739e+00  1.252654e-01 -1.548063e-01
## [273,]  6.443767e+00  7.748464e-01  6.042065e+00 -7.984766e-02 -2.555591e-01
## [274,]  6.228250e+00  2.805482e-01  6.780852e+00  2.933131e-02 -2.045714e-01
## [275,]  4.140751e+00  8.223275e-01  3.345455e+00  5.841007e-02 -2.098351e-01
## [276,]  2.391320e+00  8.096640e-01  1.787835e+00  2.080719e-01 -8.659216e-02
## [277,]  1.639585e+00  6.223235e-01  1.699148e+00  3.173357e-01  1.352291e-02
## [278,]  2.014179e+00  1.992149e-01  2.799524e+00  2.537202e-01  3.974421e-02
## [279,]  5.018840e+00  1.229129e-01  5.982103e+00  1.899409e-01 -1.386338e-01
## [280,]  3.387310e+00  4.120808e-01  3.514552e+00  1.490076e-01 -1.135660e-01
## [281,]  2.942553e+00  5.870180e-01  2.639648e+00  1.544676e-01 -1.171146e-01
## [282,]  2.885210e+00  8.250935e-02  3.864646e+00  2.112271e-01 -3.201933e-03
## [283,]  1.493011e+01  2.226987e+00  1.259221e+01 -3.763244e-01 -8.088536e-01
## [284,]  7.011514e+00 -1.031244e+00  1.152710e+01  4.575765e-01  7.893102e-02
## [285,]  1.144556e+01 -2.608512e+00  2.155968e+01  1.004904e+00  3.497838e-01
## [286,]  2.270439e+01 -3.206159e-01  2.705415e+01  2.846646e-01 -6.723340e-01
## [287,]  5.421950e+00  3.482665e-01  5.854074e+00  1.191054e-01 -1.918475e-01
## [288,] -4.240887e+00  1.223672e+00 -7.665350e+00 -7.618812e-02 -7.405355e-02
## [289,] -1.083747e+00  7.207628e-01 -2.741221e+00  8.352088e-03 -8.537266e-02
## [290,] -6.924014e-01  4.409389e-01 -1.738884e+00  1.347039e-02 -5.230048e-02
## [291,] -3.089727e-01  2.404869e-01 -9.371455e-01  1.837071e-02 -4.085677e-02
## [292,] -8.469696e-02  1.400547e-01 -6.051568e-01  3.224612e-03 -4.950283e-02
## [293,] -1.623849e-01  1.804575e-01 -6.655686e-01  1.068359e-03 -3.780237e-02
## [294,] -3.444233e-01  2.700202e-01 -9.561547e-01  1.837312e-02 -3.365191e-02
## [295,] -3.644325e-01  2.614830e-01 -9.729167e-01  6.463118e-03 -3.152730e-02
## [296,] -2.229528e-01  1.516963e-01 -5.858027e-01  1.548813e-02 -2.029662e-02
## [297,] -4.313518e-02  8.157165e-02 -2.916186e-01  2.686100e-04 -2.231991e-02
## [298,] -2.672562e-02  5.500919e-02 -2.054962e-01  1.616603e-03 -1.700692e-02
## [299,] -4.943808e-02  5.562579e-02 -2.194423e-01  2.829664e-03 -1.418830e-02
## [300,] -7.303012e-02  5.310219e-02 -2.235416e-01  6.769918e-03 -1.079002e-02
## [301,] -6.518492e-04  2.327970e-02 -1.311448e-01  1.849460e-03 -1.521491e-02
## [302,] -2.264435e-03  5.218045e-02 -2.021207e-01 -9.048749e-04 -2.091876e-02
## [303,] -1.377532e-02  1.073046e-02 -1.041848e-01  3.604440e-03 -1.042958e-02
## [304,] -3.405044e-02  3.194140e-02 -1.523309e-01  4.417764e-03 -1.096165e-02
## [305,] -2.900058e-02 -4.930446e-03 -4.164375e-02  5.423253e-03 -8.502730e-04
## [306,] -1.773617e-02 -6.290285e-03 -2.855683e-02  3.738920e-03 -1.304043e-03
## [307,] -3.132208e-02 -8.637165e-03 -2.916536e-02  5.931015e-03  8.629200e-04
## [308,] -2.317975e-02 -8.246378e-03 -1.543262e-02  4.418787e-03  1.280647e-03
## [309,] -2.447973e-03  3.284088e-03 -3.426827e-02  7.933002e-04 -3.877663e-03
## [310,] -1.264816e-02  6.016933e-03 -6.685526e-02  2.591178e-03 -6.119404e-03
## [311,] -2.653136e-02  2.178570e-02 -1.061366e-01  2.853423e-03 -7.089356e-03
## [312,] -4.544358e-02  1.068344e-02 -8.024874e-02  5.614561e-03 -1.191814e-03
## [313,] -1.999482e-02  2.273352e-02 -1.071967e-01  1.439484e-03 -8.261610e-03
## [314,] -5.780437e-02  3.974002e-02 -1.656214e-01  5.296339e-03 -7.286030e-03
## [315,] -2.231824e-02  3.662859e-02 -1.413277e-01  1.567450e-03 -1.109419e-02
## [316,] -4.796192e-02  3.040373e-02 -1.414416e-01  5.002131e-03 -6.902261e-03
## [317,] -5.870742e-02  5.458451e-02 -1.998467e-01  4.078764e-03 -1.011331e-02
## [318,] -9.041686e-02  9.583897e-02 -3.487006e-01  4.779362e-03 -1.952352e-02
## [319,] -1.956528e-01  1.414689e-01 -5.385105e-01  1.293072e-02 -2.013084e-02
## [320,] -1.605307e-01  1.511133e-01 -5.392811e-01  6.205921e-03 -2.556217e-02
## [321,] -2.232503e-01  1.779638e-01 -6.481685e-01  9.243396e-03 -2.513456e-02
## [322,] -2.210652e-01  1.720215e-01 -6.292033e-01  1.158244e-02 -2.406278e-02
## [323,] -2.363729e-01  1.873788e-01 -6.811062e-01  1.021580e-02 -2.617716e-02
## [324,] -1.691100e-01  1.615230e-01 -5.749793e-01  7.881370e-03 -2.790404e-02
## [325,] -1.201563e-01  1.217117e-01 -4.503399e-01  6.072088e-03 -2.453404e-02
## [326,] -2.550904e-01  1.968842e-01 -8.439309e-01  1.615023e-02 -4.316299e-02
## [327,] -2.010633e-01  1.725413e-01 -6.318852e-01  9.603884e-03 -2.784372e-02
## [328,] -2.314954e-01  1.624240e-01 -6.121888e-01  1.902457e-02 -2.104943e-02
## [329,] -2.106828e-01  1.533521e-01 -5.708797e-01  1.123371e-02 -2.012183e-02
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## [473,]  6.695626e-03
## [474,]  3.126680e-02
## [475,] -5.235967e-02
## [476,]  2.050393e-02
## [477,]  2.524886e-02
## [478,]  1.101139e-01
## [479,] -2.671139e-02
## [480,]  1.967578e-01
## [481,] -3.266136e-02
## [482,] -1.374602e-01
## [483,]  1.525866e-01
## [484,] -1.996779e-02
## [485,] -1.126456e-02
## [486,]  9.228966e-02
## [487,]  2.368848e-01
## [488,]  2.218055e-02
## [489,] -1.490072e-01
## [490,]  1.841799e-01
## [491,] -1.931883e-01
## [492,] -3.304760e-02
## [493,] -2.885833e-01
## [494,]  2.357041e-01
## [495,] -1.183782e-02
## [496,]  9.340974e-02
## [497,]  1.375981e+00
## [498,]  2.866931e-01
## [499,] -2.671411e-01
## [500,] -1.157794e-01
## [501,] -2.169656e-01
## [502,] -2.931846e-01
## [503,] -1.934884e-01
## [504,] -2.151833e-01
## 
## 
## $startweights
## $startweights[[1]]
## $startweights[[1]][[1]]
##            [,1]        [,2]       [,3]       [,4]       [,5]
## [1,]  0.7016638  1.42120777 -0.6478073 -0.7331516  0.4082279
## [2,] -0.3194949 -0.97422323 -1.8958292 -1.2224729  0.7551995
## [3,] -1.2020558 -1.58123176  0.9817489 -2.2977949 -1.6499259
## [4,]  0.5475972  2.26877259 -0.2627624 -0.6596883  1.5312134
## [5,]  0.6332179 -0.09044432  0.4463861 -1.3103598  0.4622648
## [6,]  1.2744752  1.11248282  2.3894857 -0.1322980 -1.3112124
## [7,] -0.5065648  0.95368358  2.0997473 -0.6568187 -0.7136369
## 
## $startweights[[1]][[2]]
##            [,1]       [,2]       [,3]       [,4]        [,5]
## [1,] -0.2230966 -0.5002760  0.4917620  0.1868573  1.57595001
## [2,]  2.8306276  1.9938733 -0.4845402  0.6097446 -1.49473588
## [3,]  0.5006493 -1.7194500  0.8754092 -1.1223845 -1.54268309
## [4,] -0.2503904  0.2040120 -0.7335978  0.9520710 -0.07828294
## [5,]  0.1373401 -0.2571040 -1.1171558  2.0194287  1.19845728
## [6,] -1.1154681 -0.9134846  0.5355775  1.1670140 -0.99000658
## 
## $startweights[[1]][[3]]
##            [,1]
## [1,]  0.6237192
## [2,]  1.1284937
## [3,] -0.3004508
## [4,]  0.4809396
## [5,] -0.2726058
## [6,]  0.3367081
## 
## 
## 
## $result.matrix
##                              [,1]
## error                  3.41974814
## reached.threshold      0.64351055
## steps                 57.00000000
## Intercept.to.1layhid1 -1.04347561
## open.to.1layhid1       0.10536819
## high.to.1layhid1      -0.77796999
## low.to.1layhid1        0.97115197
## close.to.1layhid1      1.05725371
## volume.to.1layhid1    -0.10571861
## adjusted.to.1layhid1  -0.20694569
## Intercept.to.1layhid2  1.21966837
## open.to.1layhid2      -0.56433335
## high.to.1layhid2      -1.34214359
## low.to.1layhid2        2.50125525
## close.to.1layhid2      0.14203833
## volume.to.1layhid2     0.30486351
## adjusted.to.1layhid2   1.19277175
## Intercept.to.1layhid3 -0.78852282
## open.to.1layhid3      -2.56682999
## high.to.1layhid3       0.35575588
## low.to.1layhid3       -0.74203483
## close.to.1layhid3      0.29812283
## volume.to.1layhid3     0.42470340
## adjusted.to.1layhid3   1.92322980
## Intercept.to.1layhid4 -0.83925635
## open.to.1layhid4      -0.49084197
## high.to.1layhid4      -1.56616405
## low.to.1layhid4        0.07439797
## close.to.1layhid4      0.27699771
## volume.to.1layhid4    -0.08247150
## adjusted.to.1layhid4   0.56119876
## Intercept.to.1layhid5  1.97788198
## open.to.1layhid5       0.28776379
## high.to.1layhid5      -2.13196245
## low.to.1layhid5        1.04617798
## close.to.1layhid5      0.00666424
## volume.to.1layhid5    -0.33767441
## adjusted.to.1layhid5  -1.18865364
## Intercept.to.2layhid1 -0.46795961
## 1layhid1.to.2layhid1   2.62471809
## 1layhid2.to.2layhid1   0.41854407
## 1layhid3.to.2layhid1  -1.49490107
## 1layhid4.to.2layhid1  -0.35645919
## 1layhid5.to.2layhid1  -2.51469942
## Intercept.to.2layhid2 -0.32643745
## 1layhid1.to.2layhid2   1.70540589
## 1layhid2.to.2layhid2  -2.17976339
## 1layhid3.to.2layhid2   1.78216089
## 1layhid4.to.2layhid2   0.36789034
## 1layhid5.to.2layhid2  -0.03544756
## Intercept.to.2layhid3  0.42300551
## 1layhid1.to.2layhid3  -0.19607280
## 1layhid2.to.2layhid3   1.33572258
## 1layhid3.to.2layhid3  -1.03220336
## 1layhid4.to.2layhid3  -1.67061813
## 1layhid5.to.2layhid3   0.27113713
## Intercept.to.2layhid4 -0.07637781
## 1layhid1.to.2layhid4   0.24260443
## 1layhid2.to.2layhid4  -1.38561966
## 1layhid3.to.2layhid4   1.00135788
## 1layhid4.to.2layhid4   2.35557244
## 1layhid5.to.2layhid4   1.93647719
## Intercept.to.2layhid5  1.23633296
## 1layhid1.to.2layhid5  -1.93313676
## 1layhid2.to.2layhid5  -2.40299652
## 1layhid3.to.2layhid5   0.53212871
## 1layhid4.to.2layhid5   0.96341657
## 1layhid5.to.2layhid5  -0.82649535
## Intercept.to.closem1   0.42611201
## 2layhid1.to.closem1    1.53500623
## 2layhid2.to.closem1   -0.72787019
## 2layhid3.to.closem1    0.79884062
## 2layhid4.to.closem1   -0.74191489
## 2layhid5.to.closem1   -1.37260397
## 
## attr(,"class")
## [1] "nn"
plot(nn_a)

12 - Fazendo a previsão.

prev_a <- predict(nn_a, dat_scale_a)
head(prev_a)
##           [,1]
## [1,] -1.758253
## [2,] -1.682054
## [3,] -1.718796
## [4,] -1.640107
## [5,] -1.563304
## [6,] -1.574928

13 - Fazendo a desnormalização da previsão.

prev = unscale(prev, dat_scale).

prev_a = DesPadronizacao(prev_a, mean(dat_a[,'close']), sd(dat_a[,'close']))
print(prev_a)
##            [,1]
##   [1,] 58.77214
##   [2,] 59.44242
##   [3,] 59.11922
##   [4,] 59.81141
##   [5,] 60.48700
##   [6,] 60.38476
##   [7,] 59.45385
##   [8,] 60.12457
##   [9,] 60.08013
##  [10,] 60.93780
##  [11,] 60.72835
##  [12,] 60.49249
##  [13,] 61.87314
##  [14,] 62.32887
##  [15,] 62.31433
##  [16,] 61.75882
##  [17,] 62.94106
##  [18,] 62.89838
##  [19,] 63.69066
##  [20,] 64.56563
##  [21,] 64.65200
##  [22,] 65.39852
##  [23,] 64.70417
##  [24,] 65.33664
##  [25,] 64.85121
##  [26,] 65.94610
##  [27,] 65.34127
##  [28,] 65.58722
##  [29,] 66.21658
##  [30,] 67.09977
##  [31,] 67.23946
##  [32,] 67.70992
##  [33,] 68.39195
##  [34,] 68.52963
##  [35,] 69.54015
##  [36,] 70.41832
##  [37,] 70.49769
##  [38,] 70.02190
##  [39,] 69.06797
##  [40,] 68.71162
##  [41,] 68.31766
##  [42,] 67.76765
##  [43,] 68.33035
##  [44,] 68.46134
##  [45,] 66.72626
##  [46,] 66.65853
##  [47,] 67.76558
##  [48,] 69.14286
##  [49,] 69.70886
##  [50,] 69.43188
##  [51,] 69.13238
##  [52,] 68.13138
##  [53,] 68.56682
##  [54,] 67.96211
##  [55,] 67.60089
##  [56,] 67.83510
##  [57,] 67.53024
##  [58,] 67.63486
##  [59,] 67.67865
##  [60,] 68.06035
##  [61,] 69.37946
##  [62,] 69.73409
##  [63,] 70.44180
##  [64,] 69.46593
##  [65,] 69.42936
##  [66,] 69.64956
##  [67,] 69.12041
##  [68,] 68.66998
##  [69,] 68.74280
##  [70,] 69.31537
##  [71,] 70.67166
##  [72,] 71.12119
##  [73,] 70.89612
##  [74,] 72.05433
##  [75,] 72.83614
##  [76,] 70.89480
##  [77,] 71.52444
##  [78,] 71.74682
##  [79,] 71.04373
##  [80,] 71.41961
##  [81,] 72.05080
##  [82,] 71.71554
##  [83,] 72.41619
##  [84,] 71.49254
##  [85,] 71.54208
##  [86,] 70.30487
##  [87,] 69.22928
##  [88,] 69.22009
##  [89,] 68.18598
##  [90,] 68.48346
##  [91,] 67.99460
##  [92,] 67.94112
##  [93,] 68.13298
##  [94,] 70.01993
##  [95,] 69.76192
##  [96,] 68.86905
##  [97,] 69.95380
##  [98,] 69.12374
##  [99,] 67.63896
## [100,] 67.48744
## [101,] 67.81390
## [102,] 66.58201
## [103,] 67.84518
## [104,] 66.33908
## [105,] 66.25200
## [106,] 66.69841
## [107,] 69.00715
## [108,] 69.23360
## [109,] 70.35669
## [110,] 70.83166
## [111,] 70.73394
## [112,] 70.10886
## [113,] 70.19469
## [114,] 69.80796
## [115,] 69.66376
## [116,] 70.13068
## [117,] 71.47511
## [118,] 72.72094
## [119,] 72.69473
## [120,] 71.69116
## [121,] 72.03507
## [122,] 71.74476
## [123,] 71.20968
## [124,] 71.97005
## [125,] 72.77426
## [126,] 72.02168
## [127,] 71.39831
## [128,] 70.41765
## [129,] 71.07791
## [130,] 71.14203
## [131,] 71.19102
## [132,] 70.74679
## [133,] 71.98000
## [134,] 72.17336
## [135,] 72.20425
## [136,] 71.69871
## [137,] 69.74957
## [138,] 70.61160
## [139,] 70.30708
## [140,] 67.82574
## [141,] 69.79270
## [142,] 72.45130
## [143,] 72.09892
## [144,] 72.36994
## [145,] 70.04697
## [146,] 70.79488
## [147,] 68.65619
## [148,] 67.34769
## [149,] 64.97651
## [150,] 64.82504
## [151,] 64.17331
## [152,] 65.46802
## [153,] 65.20636
## [154,] 64.62279
## [155,] 63.54896
## [156,] 64.03204
## [157,] 63.31445
## [158,] 64.09687
## [159,] 64.98661
## [160,] 64.62644
## [161,] 64.85905
## [162,] 64.50161
## [163,] 63.04431
## [164,] 62.88072
## [165,] 63.53034
## [166,] 62.92800
## [167,] 64.78138
## [168,] 65.69415
## [169,] 64.19066
## [170,] 64.99471
## [171,] 66.57657
## [172,] 68.97587
## [173,] 68.93100
## [174,] 71.13428
## [175,] 71.77894
## [176,] 72.23062
## [177,] 71.38273
## [178,] 71.63242
## [179,] 71.04740
## [180,] 70.90465
## [181,] 71.30798
## [182,] 71.32141
## [183,] 69.64703
## [184,] 69.49063
## [185,] 68.81479
## [186,] 68.18997
## [187,] 68.92456
## [188,] 69.83368
## [189,] 68.14933
## [190,] 66.32488
## [191,] 65.32990
## [192,] 66.94619
## [193,] 67.38967
## [194,] 66.73471
## [195,] 66.67841
## [196,] 67.28445
## [197,] 69.51551
## [198,] 68.90137
## [199,] 69.23183
## [200,] 70.87441
## [201,] 71.24996
## [202,] 71.40427
## [203,] 73.52532
## [204,] 73.80644
## [205,] 75.67008
## [206,] 75.54882
## [207,] 75.95639
## [208,] 77.56547
## [209,] 77.33992
## [210,] 76.78919
## [211,] 76.19324
## [212,] 76.88866
## [213,] 78.77754
## [214,] 79.52925
## [215,] 79.24824
## [216,] 80.10081
## [217,] 79.47269
## [218,] 79.54907
## [219,] 79.60026
## [220,] 78.92198
## [221,] 79.71769
## [222,] 80.04762
## [223,] 79.56482
## [224,] 79.50744
## [225,] 78.84989
## [226,] 79.46696
## [227,] 79.94043
## [228,] 80.84185
## [229,] 81.40668
## [230,] 81.48446
## [231,] 81.14998
## [232,] 81.18821
## [233,] 79.63313
## [234,] 80.18345
## [235,] 79.97220
## [236,] 80.84217
## [237,] 80.64585
## [238,] 80.90413
## [239,] 81.17907
## [240,] 82.19800
## [241,] 82.32340
## [242,] 83.04538
## [243,] 82.87347
## [244,] 82.38705
## [245,] 80.19382
## [246,] 78.60275
## [247,] 79.69149
## [248,] 79.08940
## [249,] 79.08589
## [250,] 79.30799
## [251,] 78.89712
## [252,] 78.94286
## [253,] 79.01531
## [254,] 78.14595
## [255,] 77.98374
## [256,] 77.70672
## [257,] 77.98486
## [258,] 78.43079
## [259,] 78.25891
## [260,] 77.80931
## [261,] 78.50610
## [262,] 77.76978
## [263,] 77.85778
## [264,] 78.65723
## [265,] 78.04888
## [266,] 77.50087
## [267,] 76.48220
## [268,] 77.02271
## [269,] 75.51883
## [270,] 75.69931
## [271,] 77.02908
## [272,] 75.57285
## [273,] 75.06319
## [274,] 75.04065
## [275,] 75.67106
## [276,] 77.25822
## [277,] 77.96593
## [278,] 76.60735
## [279,] 75.18776
## [280,] 75.78905
## [281,] 76.25464
## [282,] 75.85457
## [283,] 74.58396
## [284,] 74.79740
## [285,] 74.54669
## [286,] 74.44836
## [287,] 75.16732
## [288,] 73.39759
## [289,] 71.62514
## [290,] 70.42467
## [291,] 68.26354
## [292,] 66.84717
## [293,] 66.54479
## [294,] 67.96819
## [295,] 68.15927
## [296,] 65.80598
## [297,] 62.43834
## [298,] 61.12825
## [299,] 61.46951
## [300,] 61.66953
## [301,] 59.99630
## [302,] 61.08882
## [303,] 59.66379
## [304,] 60.40816
## [305,] 58.35794
## [306,] 57.88121
## [307,] 58.10104
## [308,] 57.55494
## [309,] 57.64984
## [310,] 58.68621
## [311,] 59.46479
## [312,] 59.19469
## [313,] 59.44109
## [314,] 60.67239
## [315,] 60.00378
## [316,] 60.26195
## [317,] 61.12702
## [318,] 63.24318
## [319,] 65.35221
## [320,] 65.27341
## [321,] 66.16043
## [322,] 66.03436
## [323,] 66.41106
## [324,] 65.64331
## [325,] 64.43970
## [326,] 68.16726
## [327,] 66.06083
## [328,] 66.03689
## [329,] 65.61027
## [330,] 66.02689
## [331,] 66.04836
## [332,] 68.04468
## [333,] 70.30270
## [334,] 70.16746
## [335,] 68.92734
## [336,] 67.84455
## [337,] 67.58062
## [338,] 68.62880
## [339,] 68.78985
## [340,] 68.96574
## [341,] 69.48622
## [342,] 69.11952
## [343,] 67.32068
## [344,] 67.31337
## [345,] 64.24204
## [346,] 67.33877
## [347,] 69.40828
## [348,] 69.38450
## [349,] 69.25044
## [350,] 68.67359
## [351,] 67.87961
## [352,] 69.69186
## [353,] 73.83391
## [354,] 74.71754
## [355,] 73.57275
## [356,] 73.98743
## [357,] 73.36766
## [358,] 74.28976
## [359,] 74.36837
## [360,] 76.71623
## [361,] 77.75338
## [362,] 77.88805
## [363,] 77.88608
## [364,] 74.26978
## [365,] 72.91792
## [366,] 70.99960
## [367,] 75.11784
## [368,] 74.88027
## [369,] 74.68868
## [370,] 74.84192
## [371,] 74.25357
## [372,] 75.46846
## [373,] 73.42160
## [374,] 72.22875
## [375,] 73.11572
## [376,] 72.94803
## [377,] 73.05542
## [378,] 75.16687
## [379,] 75.41181
## [380,] 76.74371
## [381,] 76.37057
## [382,] 75.74301
## [383,] 75.10658
## [384,] 74.73171
## [385,] 75.25786
## [386,] 75.02290
## [387,] 77.74355
## [388,] 78.10787
## [389,] 78.89008
## [390,] 78.66906
## [391,] 78.66705
## [392,] 84.66621
## [393,] 86.00472
## [394,] 86.83854
## [395,] 86.44960
## [396,] 86.41931
## [397,] 85.88979
## [398,] 84.91484
## [399,] 84.79026
## [400,] 85.69306
## [401,] 85.06487
## [402,] 86.48819
## [403,] 87.13203
## [404,] 87.88461
## [405,] 88.57727
## [406,] 89.72280
## [407,] 89.68233
## [408,] 89.10971
## [409,] 88.64436
## [410,] 88.89588
## [411,] 88.58063
## [412,] 88.41186
## [413,] 87.63314
## [414,] 87.20465
## [415,] 87.57400
## [416,] 87.35727
## [417,] 86.70967
## [418,] 87.08234
## [419,] 86.32103
## [420,] 86.38511
## [421,] 86.68768
## [422,] 87.70175
## [423,] 87.74640
## [424,] 86.48907
## [425,] 84.83882
## [426,] 84.73205
## [427,] 84.46044
## [428,] 83.38316
## [429,] 85.75363
## [430,] 86.73898
## [431,] 86.95559
## [432,] 86.04709
## [433,] 85.99224
## [434,] 82.72798
## [435,] 81.87066
## [436,] 82.47454
## [437,] 81.57472
## [438,] 83.02930
## [439,] 85.85675
## [440,] 86.63871
## [441,] 86.90685
## [442,] 85.54812
## [443,] 84.59819
## [444,] 87.24894
## [445,] 88.48290
## [446,] 88.90401
## [447,] 88.97796
## [448,] 89.45427
## [449,] 89.39629
## [450,] 88.84665
## [451,] 88.73930
## [452,] 88.36380
## [453,] 89.29089
## [454,] 89.30975
## [455,] 88.87940
## [456,] 89.43532
## [457,] 90.26855
## [458,] 90.39822
## [459,] 89.88198
## [460,] 89.48681
## [461,] 86.85886
## [462,] 85.70422
## [463,] 85.52518
## [464,] 87.71198
## [465,] 89.70535
## [466,] 90.05429
## [467,] 89.31952
## [468,] 89.86422
## [469,] 90.71140
## [470,] 89.54079
## [471,] 90.01290
## [472,] 89.12085
## [473,] 89.23309
## [474,] 89.58197
## [475,] 88.71049
## [476,] 89.29472
## [477,] 89.06717
## [478,] 88.59976
## [479,] 89.45629
## [480,] 88.95235
## [481,] 89.39293
## [482,] 88.29924
## [483,] 87.55701
## [484,] 88.11894
## [485,] 87.45261
## [486,] 87.31839
## [487,] 86.94361
## [488,] 86.70481
## [489,] 86.40313
## [490,] 87.13300
## [491,] 86.58781
## [492,] 85.46054
## [493,] 85.64591
## [494,] 86.94562
## [495,] 87.26733
## [496,] 86.64913
## [497,] 85.39175
## [498,] 85.46620
## [499,] 85.36168
## [500,] 85.13750
## [501,] 85.53092
## [502,] 86.24073
## [503,] 85.84994
## [504,] 85.57580
mean(prev_a)
## [1] 74.2382
sd(prev_a)
## [1] 8.688789
plot(as.vector(Cl(PCAR)), type = 'l')
lines(prev_a, col='red')

accuracy(as.vector(prev_a), Cl(PCAR))
##                    ME      RMSE       MAE         MPE      MAPE
## Test set 0.0004334916 0.9940446 0.6959047 -0.03048003 0.9712447

14 - Fazendo a previsão - previsão dia-a-dia.

Estes dados são para testar a rede - com 1 ano de diferença.

getSymbols("PCAR", src='yahoo', from='2018-01-01', to='2019-12-31')
## [1] "PCAR"
names(PCAR) <- c("open", "high", "low", "close", "volume", "adjusted")

dat_t = data.frame(PCAR)

15 - Vou prever o fechamento, então devo deslocar um dia.

dat_t['closem1'] <- as.numeric(Lag(Cl(PCAR),1))

16 - Preenchendo as NA existentes na tabela - também poderíamos eliminar.

dat_t <- na.fill(dat_t,"extend")
head(dat_t)
##       open  high   low close  volume adjusted closem1
## [1,] 72.57 73.57 72.46 73.02 2251500 62.07004   73.02
## [2,] 72.47 73.94 72.42 73.91 1428600 62.82659   73.02
## [3,] 74.19 75.69 74.00 74.85 3095100 63.62561   73.91
## [4,] 75.32 75.37 74.44 75.10 2024500 63.83814   74.85
## [5,] 74.86 75.24 74.72 74.86 2075600 63.63411   75.10
## [6,] 74.86 75.10 74.45 74.83 2869700 63.60862   74.86

17 - Normalizando os dados - é necessário que eles estejam na mesma escala.

dat_scale_t <- matrix(0, nrow = dim(dat_t)[1], ncol = dim(dat_t)[2])
colnames(dat_scale_t) <- c("open", "high", "low", "close", "volume", "adjusted", "closem1")
dat_scale_t[,1] <- PadraodeDados(dat_t[,1])
dat_scale_t[,2] <- PadraodeDados(dat_t[,2])
dat_scale_t[,3] <- PadraodeDados(dat_t[,3])
dat_scale_t[,4] <- PadraodeDados(dat_t[,4])
dat_scale_t[,5] <- PadraodeDados(dat_t[,5])
dat_scale_t[,6] <- PadraodeDados(dat_t[,6])
dat_scale_t[,7] <- PadraodeDados(dat_t[,7])

18 - Realizando a previsão com a base teste, de 502 linhas.

prev_t <- predict(nn_a, dat_scale_t)

19 - Testando a previsão realizada.

prev_t = DesPadronizacao(prev_t, mean(dat_t[,'close']), sd(dat_t[,'close']))
print(prev_t)
##            [,1]
##   [1,] 72.97465
##   [2,] 73.04120
##   [3,] 75.24033
##   [4,] 74.96888
##   [5,] 74.81196
##   [6,] 75.05521
##   [7,] 74.77024
##   [8,] 75.42222
##   [9,] 76.22649
##  [10,] 76.55120
##  [11,] 76.06580
##  [12,] 76.39589
##  [13,] 76.99940
##  [14,] 77.27804
##  [15,] 76.53862
##  [16,] 77.59205
##  [17,] 77.63558
##  [18,] 77.80959
##  [19,] 78.43618
##  [20,] 75.73820
##  [21,] 76.00669
##  [22,] 74.47415
##  [23,] 73.21195
##  [24,] 70.39938
##  [25,] 67.92695
##  [26,] 69.78470
##  [27,] 68.39242
##  [28,] 66.54692
##  [29,] 68.67821
##  [30,] 68.07227
##  [31,] 68.08124
##  [32,] 69.28234
##  [33,] 69.28309
##  [34,] 69.00337
##  [35,] 69.77748
##  [36,] 70.30912
##  [37,] 71.14225
##  [38,] 71.56734
##  [39,] 71.93508
##  [40,] 71.80567
##  [41,] 71.07306
##  [42,] 68.03900
##  [43,] 67.25140
##  [44,] 70.32253
##  [45,] 69.11113
##  [46,] 68.93958
##  [47,] 68.68438
##  [48,] 68.93442
##  [49,] 68.28495
##  [50,] 67.21649
##  [51,] 67.42138
##  [52,] 67.34480
##  [53,] 66.58301
##  [54,] 67.13572
##  [55,] 66.86046
##  [56,] 65.40065
##  [57,] 63.84308
##  [58,] 64.56315
##  [59,] 64.59264
##  [60,] 64.62288
##  [61,] 65.40834
##  [62,] 64.52420
##  [63,] 65.14427
##  [64,] 64.69639
##  [65,] 68.49467
##  [66,] 66.61778
##  [67,] 66.72366
##  [68,] 67.37548
##  [69,] 67.75542
##  [70,] 67.98212
##  [71,] 68.55260
##  [72,] 69.09477
##  [73,] 70.26284
##  [74,] 70.82620
##  [75,] 70.21259
##  [76,] 69.80209
##  [77,] 69.84600
##  [78,] 65.16564
##  [79,] 64.87546
##  [80,] 64.72678
##  [81,] 64.54197
##  [82,] 64.67319
##  [83,] 62.69545
##  [84,] 61.80762
##  [85,] 60.72308
##  [86,] 61.61964
##  [87,] 64.59534
##  [88,] 64.48030
##  [89,] 64.30542
##  [90,] 64.57172
##  [91,] 63.97316
##  [92,] 63.92920
##  [93,] 63.17281
##  [94,] 63.86417
##  [95,] 63.93418
##  [96,] 64.66865
##  [97,] 65.68807
##  [98,] 66.59750
##  [99,] 65.07175
## [100,] 65.26393
## [101,] 64.84780
## [102,] 63.61461
## [103,] 64.02571
## [104,] 62.71330
## [105,] 63.00276
## [106,] 63.12259
## [107,] 64.45647
## [108,] 64.79505
## [109,] 65.57946
## [110,] 65.49871
## [111,] 65.69665
## [112,] 65.84926
## [113,] 64.62237
## [114,] 64.07224
## [115,] 63.50184
## [116,] 63.56362
## [117,] 62.73661
## [118,] 63.01131
## [119,] 61.59395
## [120,] 62.20727
## [121,] 61.73030
## [122,] 62.37608
## [123,] 62.00009
## [124,] 61.43404
## [125,] 62.03656
## [126,] 60.16394
## [127,] 60.65880
## [128,] 60.63748
## [129,] 60.44377
## [130,] 61.06570
## [131,] 62.41053
## [132,] 61.36942
## [133,] 61.30371
## [134,] 61.73776
## [135,] 63.07417
## [136,] 62.28216
## [137,] 62.09099
## [138,] 63.29601
## [139,] 63.23860
## [140,] 63.03090
## [141,] 62.24556
## [142,] 62.39058
## [143,] 64.95915
## [144,] 66.33175
## [145,] 65.88347
## [146,] 66.18975
## [147,] 64.44665
## [148,] 63.47896
## [149,] 63.68412
## [150,] 65.74009
## [151,] 63.41458
## [152,] 64.96312
## [153,] 64.37151
## [154,] 63.63268
## [155,] 64.09185
## [156,] 64.70763
## [157,] 64.03688
## [158,] 65.79105
## [159,] 66.42111
## [160,] 68.23387
## [161,] 68.83528
## [162,] 68.45960
## [163,] 67.17629
## [164,] 67.21986
## [165,] 68.31718
## [166,] 69.23087
## [167,] 68.67134
## [168,] 68.50358
## [169,] 68.43094
## [170,] 68.61167
## [171,] 68.45417
## [172,] 69.87530
## [173,] 69.15266
## [174,] 69.99767
## [175,] 69.76612
## [176,] 69.21837
## [177,] 70.07061
## [178,] 70.40742
## [179,] 70.98130
## [180,] 70.64618
## [181,] 71.38114
## [182,] 72.06190
## [183,] 72.58344
## [184,] 70.95828
## [185,] 69.99859
## [186,] 69.58092
## [187,] 69.23311
## [188,] 69.05080
## [189,] 69.33256
## [190,] 69.47121
## [191,] 70.24312
## [192,] 70.17605
## [193,] 68.42598
## [194,] 68.33617
## [195,] 67.16442
## [196,] 64.79873
## [197,] 63.17845
## [198,] 62.25511
## [199,] 62.10805
## [200,] 62.13102
## [201,] 61.78300
## [202,] 60.52018
## [203,] 60.26138
## [204,] 61.04223
## [205,] 57.63143
## [206,] 57.81002
## [207,] 57.31831
## [208,] 56.85572
## [209,] 57.42489
## [210,] 57.54845
## [211,] 58.05975
## [212,] 58.14898
## [213,] 59.08100
## [214,] 58.87751
## [215,] 58.83312
## [216,] 59.09536
## [217,] 59.65289
## [218,] 59.23826
## [219,] 58.82983
## [220,] 59.14560
## [221,] 59.23172
## [222,] 58.94959
## [223,] 60.09218
## [224,] 60.36207
## [225,] 59.75778
## [226,] 59.77850
## [227,] 59.31831
## [228,] 60.53411
## [229,] 60.33697
## [230,] 60.11719
## [231,] 61.43821
## [232,] 61.71870
## [233,] 61.99708
## [234,] 60.05509
## [235,] 58.04281
## [236,] 58.47191
## [237,] 57.68286
## [238,] 58.59035
## [239,] 58.99896
## [240,] 57.93115
## [241,] 57.76037
## [242,] 57.43000
## [243,] 57.53049
## [244,] 57.32233
## [245,] 56.99430
## [246,] 57.20803
## [247,] 57.00995
## [248,] 56.62676
## [249,] 57.07439
## [250,] 58.01806
## [251,] 57.88729
## [252,] 57.28959
## [253,] 57.77437
## [254,] 57.92203
## [255,] 58.61704
## [256,] 59.53521
## [257,] 59.21187
## [258,] 58.29522
## [259,] 58.84432
## [260,] 58.94750
## [261,] 59.60595
## [262,] 59.55788
## [263,] 59.66227
## [264,] 61.50030
## [265,] 61.93278
## [266,] 61.68553
## [267,] 61.35725
## [268,] 62.81556
## [269,] 62.21644
## [270,] 63.24442
## [271,] 64.56051
## [272,] 64.69331
## [273,] 65.60367
## [274,] 64.86691
## [275,] 65.52011
## [276,] 65.00341
## [277,] 65.89416
## [278,] 65.64127
## [279,] 66.00837
## [280,] 66.31265
## [281,] 67.34897
## [282,] 67.33303
## [283,] 67.48899
## [284,] 68.16589
## [285,] 68.37075
## [286,] 68.91911
## [287,] 69.53972
## [288,] 70.01227
## [289,] 69.20171
## [290,] 68.70744
## [291,] 68.23076
## [292,] 68.04560
## [293,] 67.65380
## [294,] 67.79508
## [295,] 67.83886
## [296,] 66.72659
## [297,] 66.63660
## [298,] 67.51981
## [299,] 68.30020
## [300,] 68.43487
## [301,] 68.51514
## [302,] 68.32636
## [303,] 67.76708
## [304,] 68.60215
## [305,] 68.05640
## [306,] 67.64706
## [307,] 67.73438
## [308,] 67.70971
## [309,] 67.84265
## [310,] 67.83471
## [311,] 68.15277
## [312,] 68.70605
## [313,] 68.62378
## [314,] 68.45013
## [315,] 68.56499
## [316,] 68.01022
## [317,] 69.30252
## [318,] 68.92541
## [319,] 68.54520
## [320,] 68.64779
## [321,] 68.94260
## [322,] 70.00498
## [323,] 70.45664
## [324,] 70.34751
## [325,] 71.46075
## [326,] 71.50328
## [327,] 70.65810
## [328,] 70.87119
## [329,] 70.98028
## [330,] 70.44542
## [331,] 70.26866
## [332,] 70.84226
## [333,] 71.43717
## [334,] 71.55247
## [335,] 70.54901
## [336,] 70.60394
## [337,] 69.67373
## [338,] 68.52693
## [339,] 68.87802
## [340,] 68.02716
## [341,] 68.33504
## [342,] 67.70690
## [343,] 67.74587
## [344,] 67.95960
## [345,] 69.58505
## [346,] 69.37596
## [347,] 68.68025
## [348,] 69.56236
## [349,] 69.19233
## [350,] 67.37565
## [351,] 67.60599
## [352,] 67.78723
## [353,] 66.65375
## [354,] 67.95637
## [355,] 66.54880
## [356,] 66.33569
## [357,] 66.61244
## [358,] 68.70700
## [359,] 68.97595
## [360,] 69.97272
## [361,] 70.27089
## [362,] 70.36311
## [363,] 69.69483
## [364,] 69.83141
## [365,] 69.69620
## [366,] 69.64226
## [367,] 69.85795
## [368,] 70.76445
## [369,] 71.83153
## [370,] 71.81502
## [371,] 71.17334
## [372,] 71.30283
## [373,] 71.19877
## [374,] 70.68184
## [375,] 70.69867
## [376,] 72.25149
## [377,] 71.04765
## [378,] 71.07843
## [379,] 70.21262
## [380,] 70.30968
## [381,] 70.26970
## [382,] 70.69999
## [383,] 70.24251
## [384,] 71.15028
## [385,] 71.67521
## [386,] 71.52088
## [387,] 71.51845
## [388,] 69.29812
## [389,] 69.34201
## [390,] 69.38539
## [391,] 66.85449
## [392,] 69.26188
## [393,] 71.86774
## [394,] 71.34760
## [395,] 71.58973
## [396,] 69.95169
## [397,] 70.37653
## [398,] 68.97246
## [399,] 67.38693
## [400,] 64.99944
## [401,] 64.99054
## [402,] 64.35735
## [403,] 65.72938
## [404,] 65.52849
## [405,] 64.86247
## [406,] 63.70023
## [407,] 63.93048
## [408,] 63.34137
## [409,] 64.29563
## [410,] 65.30753
## [411,] 64.82445
## [412,] 65.19883
## [413,] 64.69630
## [414,] 62.59409
## [415,] 62.68947
## [416,] 63.56876
## [417,] 62.87582
## [418,] 64.82201
## [419,] 65.88877
## [420,] 64.26292
## [421,] 65.12627
## [422,] 66.18646
## [423,] 68.59871
## [424,] 68.35435
## [425,] 70.21318
## [426,] 71.18202
## [427,] 71.42432
## [428,] 71.19971
## [429,] 70.82180
## [430,] 70.37055
## [431,] 70.30184
## [432,] 71.11330
## [433,] 70.17262
## [434,] 69.46567
## [435,] 69.33081
## [436,] 67.61426
## [437,] 67.84893
## [438,] 68.68143
## [439,] 69.20876
## [440,] 68.50159
## [441,] 66.56490
## [442,] 65.60388
## [443,] 67.07027
## [444,] 67.46556
## [445,] 66.90989
## [446,] 66.96900
## [447,] 67.52428
## [448,] 68.42547
## [449,] 68.45586
## [450,] 68.90210
## [451,] 70.01118
## [452,] 70.17699
## [453,] 70.58471
## [454,] 72.79444
## [455,] 73.14540
## [456,] 75.59335
## [457,] 75.52947
## [458,] 76.12123
## [459,] 77.31225
## [460,] 77.23812
## [461,] 76.61090
## [462,] 76.28443
## [463,] 76.85430
## [464,] 78.12267
## [465,] 78.50659
## [466,] 78.49171
## [467,] 78.73430
## [468,] 78.53261
## [469,] 78.58548
## [470,] 78.60041
## [471,] 78.30600
## [472,] 78.70442
## [473,] 78.76174
## [474,] 78.58244
## [475,] 78.65054
## [476,] 78.30309
## [477,] 78.47623
## [478,] 78.72257
## [479,] 78.93257
## [480,] 79.11410
## [481,] 79.15669
## [482,] 79.14751
## [483,] 79.14680
## [484,] 78.55064
## [485,] 78.84678
## [486,] 78.68624
## [487,] 79.03576
## [488,] 78.98348
## [489,] 79.00045
## [490,] 79.11638
## [491,] 79.03083
## [492,] 79.33710
## [493,] 79.33220
## [494,] 79.29946
## [495,] 79.21689
## [496,] 78.65359
## [497,] 76.54286
## [498,] 78.54746
## [499,] 78.36850
## [500,] 78.37768
## [501,] 78.45039
## [502,] 78.26655
mean(prev_t)
## [1] 67.61473
sd(prev_t)
## [1] 5.528046
plot(as.vector(Cl(PCAR)), type = 'l')
lines(prev_t, col='red')

accuracy(as.vector(prev_t), Cl(PCAR))
##                 ME      RMSE       MAE       MPE      MAPE
## Test set 0.1696932 0.9281172 0.6590945 0.1968649 0.9667313