Ejercicio 2
Alvirde Rodíguez José Antonio
BASE DE DATOS
library(foreign)
base = read.csv(file.choose())
class(base)## [1] "data.frame"Base de datos del precio del petróleo crudo, fijado por la Organización de países exportadores de petróleo (OPEP).
Periodicidad
Se tienen registros a partir del mes de Agosto de 1993, en una frecuencia mensual.
Unidad de medida
Dólares americanos
DECLARAR COMO SERIE DE TIEMPO
PPDP <- ts(base$Precios, frequency = 12, start = c(1993,8))
class(PPDP)## [1] "ts"GRAFICA DE STN
library(forecast)
plot(PPDP, col = "red", main = "Precios del Barril Petróleo", ylab = "Dólares", xlab ="Tiempo", lwd=2, type="l", pch=10)
MEDIAS MOVILES
MEDIA MOVIL DE ORDEN 3
library(TSA)##
## Attaching package: 'TSA'## The following objects are masked from 'package:stats':
##
## acf, arima## The following object is masked from 'package:utils':
##
## tarlibrary(fpp2)## Loading required package: ggplot2## Loading required package: fma## Loading required package: expsmoothma(PPDP, order = 3)## Jan Feb Mar Apr May Jun Jul
## 1993
## 1994 13.89333 13.84333 14.16000 14.99333 16.17667 17.11000 17.35000
## 1995 16.75333 17.22333 17.80667 18.14000 18.14333 17.28667 16.63667
## 1996 17.79000 18.30000 19.25333 19.70667 19.41000 19.05333 19.51333
## 1997 22.42333 20.99333 19.21000 18.86000 18.39000 18.54000 18.31667
## 1998 15.39667 14.07333 13.57333 13.55000 13.33667 13.07000 12.55667
## 1999 10.86667 11.78667 13.26333 15.03333 16.10667 17.14667 18.55000
## 2000 25.87667 26.67333 26.06000 26.05000 26.76000 28.33000 29.02000
## 2001 26.13333 26.07333 25.99333 26.09667 26.74667 26.44000 25.86333
## 2002 19.21667 20.92333 23.01667 24.91333 25.19667 25.30333 25.67333
## 2003 30.50667 31.33000 29.60000 27.32667 26.51333 27.52333 28.73000
## 2004 30.89000 32.12333 32.90333 34.98000 35.60333 36.99667 38.50333
## 2005 42.27667 46.24333 48.80000 49.80333 50.78667 52.69667 57.38333
## 2006 59.53000 61.03000 62.86667 65.86000 68.31333 69.80667 70.85000
## 2007 57.35667 57.22667 61.07333 63.60667 66.13667 68.98333 70.64000
## 2008 91.20000 95.30667 101.33000 111.07667 120.97000 128.99333 126.30667
## 2009 42.34667 44.11667 46.25667 51.69333 59.19333 63.99000 68.48333
## 2010 75.58667 77.06000 79.41333 79.70000 78.17667 74.97667 75.04667
## 2011 93.53667 99.75000 107.60000 110.98667 110.05333 107.28000 104.75333
## 2012 107.99667 112.51667 114.71667 111.85000 102.83000 97.19000 97.58333
## 2013 104.64333 105.08667 103.00333 100.24667 99.32000 101.45667 104.38667
## 2014 104.13667 103.65667 104.58000 104.87333 106.31667 106.43667 104.55000
## 2015 54.20000 51.57667 55.05333 57.62667 60.45333 59.38667 53.78000
## 2016 32.46000 32.71667 36.37333 41.34333 44.79333 45.92000 45.56667
## 2017 53.52000 52.94667 52.47000 50.98333 49.40667 47.90667 47.92333
## 2018 63.62667 64.62000 65.47333 68.79667 NA
## Aug Sep Oct Nov Dec
## 1993 NA 16.30000 15.87333 15.16333 14.36667
## 1994 17.02667 16.55667 16.58333 16.49667 16.64000
## 1995 16.45667 16.47000 16.56000 16.91000 17.47000
## 1996 20.76333 22.10333 22.75333 23.20667 23.08000
## 1997 18.56333 19.13333 19.26333 18.74000 17.06000
## 1998 12.99667 13.18333 12.98000 11.85000 11.24333
## 1999 20.65333 21.64000 22.93667 23.73667 24.85667
## 2000 29.84000 30.91333 31.93667 29.64333 27.83000
## 2001 25.27667 23.92000 21.54333 19.31333 18.78667
## 2002 26.93667 27.53000 26.78333 26.65333 27.72667
## 2003 28.38333 28.52333 28.33667 29.36667 30.15333
## 2004 40.52333 43.52000 43.53667 42.68333 41.38000
## 2005 59.98333 60.59000 58.30667 56.55333 57.97667
## 2006 68.79333 63.94667 59.39000 59.01333 57.55000
## 2007 73.49667 76.28667 83.35667 87.61000 90.51667
## 2008 115.68667 95.64000 75.44000 56.00000 46.39000
## 2009 68.21667 71.35333 73.32667 75.50333 76.51667
## 2010 75.51000 77.89000 80.79000 85.42000 89.07667
## 2011 103.07667 100.38667 102.02667 103.16333 105.57000
## 2012 102.76667 104.98667 103.62000 101.92333 102.48667
## 2013 107.39333 107.45000 105.60667 104.51333 103.40333
## 2014 100.37667 93.99333 86.30667 74.59000 61.60000
## 2015 48.77000 46.31000 45.45000 42.21333 36.48667
## 2016 44.68333 46.40333 46.53000 49.05667 50.49000
## 2017 50.18333 52.60333 55.93333 58.68000 62.45000
## 2018autoplot(PPDP, series ="Data") + autolayer(ma(PPDP,3), series ="3-MA")+ xlab("Años") + ylab("Dólares") + ggtitle("Precios del barril del petróleo")## Warning: Removed 2 rows containing missing values (geom_path).
p1<-autoplot(PPDP, series ="Data") + autolayer(ma(PPDP,3), series ="3-MA")+ xlab("Años") + ylab("Dólares") + ggtitle("Precios del barril del petróleo")MEDIA MOVIL DE ORDEN 5
library(TSA)
library(fpp2)
ma(PPDP, order = 5)## Jan Feb Mar Apr May Jun Jul Aug
## 1993 NA
## 1994 14.100 14.076 14.578 15.186 16.006 16.682 16.906 16.944
## 1995 16.938 17.252 17.748 17.840 17.572 17.396 17.030 16.570
## 1996 17.902 18.686 18.924 19.066 19.444 19.652 19.972 20.882
## 1997 21.798 20.896 20.046 18.984 18.566 18.440 18.596 18.730
## 1998 15.680 14.562 13.950 13.446 13.166 13.040 13.100 12.946
## 1999 11.530 12.328 13.458 14.448 16.096 17.516 18.882 20.060
## 2000 25.856 25.718 26.136 26.998 27.190 27.544 29.266 30.108
## 2001 27.150 25.828 26.298 26.500 26.012 26.172 26.070 24.706
## 2002 19.996 21.344 22.774 23.842 24.996 25.624 26.194 26.566
## 2003 29.284 29.488 29.122 28.556 27.698 27.562 27.826 28.416
## 2004 31.092 32.010 33.528 34.362 35.674 37.356 38.934 40.798
## 2005 43.980 45.682 47.440 49.624 51.934 54.124 56.334 58.406
## 2006 58.912 61.498 63.948 65.114 67.664 69.840 68.670 66.516
## 2007 58.162 59.546 60.380 63.314 66.522 68.428 70.768 74.130
## 2008 93.356 96.840 103.462 111.628 119.516 122.062 120.242 110.254
## 2009 45.532 44.794 48.156 53.214 57.780 62.776 66.390 69.576
## 2010 76.722 78.048 78.196 77.718 77.682 76.988 75.376 76.596
## 2011 94.758 101.100 104.712 107.344 109.346 107.714 104.630 102.986
## 2012 109.438 111.090 111.062 107.794 104.606 102.102 100.624 100.488
## 2013 103.524 103.060 102.696 101.624 101.148 102.276 104.258 105.470
## 2014 103.816 104.264 104.310 105.564 105.644 104.846 103.042 99.116
## 2015 58.484 54.594 54.956 57.796 57.706 56.278 54.026 50.916
## 2016 35.566 35.094 36.968 40.550 43.170 44.678 45.536 46.206
## 2017 51.344 52.724 52.178 50.694 49.356 49.164 49.322 50.328
## 2018 62.996 64.768 67.216 NA NA
## Sep Oct Nov Dec
## 1993 NA 15.574 15.104 14.680
## 1994 16.926 16.538 16.518 16.762
## 1995 16.446 16.804 17.070 17.246
## 1996 21.658 22.464 23.022 22.654
## 1997 18.964 18.716 17.976 17.064
## 1998 12.826 12.368 12.158 11.548
## 1999 21.614 22.836 23.844 24.748
## 2000 30.650 30.054 29.394 28.426
## 2001 23.050 21.794 20.460 19.414
## 2002 26.576 27.004 27.798 28.718
## 2003 28.656 28.932 29.270 30.160
## 2004 42.116 42.346 42.524 43.168
## 2005 58.636 58.648 58.762 58.364
## 2006 64.486 62.194 58.536 57.624
## 2007 78.760 81.944 86.056 89.382
## 2008 94.744 76.446 62.304 50.740
## 2009 71.256 73.298 74.396 75.678
## 2010 78.556 81.642 85.014 89.372
## 2011 102.898 102.160 103.476 105.850
## 2012 102.576 103.464 103.430 103.702
## 2013 106.048 106.092 104.880 104.094
## 2014 92.840 83.934 73.346 65.134
## 2015 47.276 43.722 40.540 37.490
## 2016 45.720 47.418 49.160 51.022
## 2017 53.080 55.786 59.044 61.146
## 2018autoplot(PPDP, series= "Data") + autolayer(ma(PPDP, 5), series="5-MA") + xlab("Años") + ylab("Dólares") + ggtitle("Precios del barril del petróleo")## Warning: Removed 4 rows containing missing values (geom_path).
p2<-autoplot(PPDP, series= "Data") + autolayer(ma(PPDP, 5), series="5-MA") + xlab("Años") + ylab("Dólares") + ggtitle("Precios del barril del petróleo")MEDIA MOVIL DE ORDEN 7
ma(PPDP, order = 7)## Jan Feb Mar Apr May Jun Jul
## 1993
## 1994 14.58571 14.55143 14.83286 15.42000 15.83000 16.17571 16.58286
## 1995 17.11571 17.39429 17.43429 17.45429 17.39286 17.30714 17.13143
## 1996 18.04143 18.46143 18.71429 18.96000 19.33714 19.98857 20.59000
## 1997 21.49714 20.89143 20.25286 19.49714 18.85000 18.59857 18.70000
## 1998 15.99143 15.13286 14.18857 13.56143 13.20286 13.16000 13.18000
## 1999 12.39714 12.79714 13.44143 14.66714 15.92857 17.63571 18.89000
## 2000 24.95714 25.70571 26.48571 26.92571 27.49000 28.18429 28.74286
## 2001 27.55286 27.00286 26.23714 26.18000 26.16000 25.87000 25.25714
## 2002 20.87714 21.58286 22.41143 23.44429 24.53429 25.72000 26.27571
## 2003 28.50143 28.29143 28.77429 28.87429 28.72143 27.86429 27.67143
## 2004 31.16857 32.39000 33.30714 34.43857 35.96857 37.43571 39.32286
## 2005 45.34571 45.48143 47.16143 49.63714 52.34000 54.75000 55.78571
## 2006 60.10286 61.60143 63.49429 65.78286 67.11857 67.46429 67.03286
## 2007 59.11143 60.14714 61.58286 63.38429 65.75714 68.50000 71.55286
## 2008 93.93000 99.73857 105.47857 111.66571 115.07714 115.97286 111.80857
## 2009 50.09000 48.01286 50.18143 53.51429 57.48143 61.26857 65.18714
## 2010 77.41000 77.63000 77.22714 77.18429 77.00000 77.19429 77.54000
## 2011 95.96429 99.72857 102.77429 105.33286 106.44714 106.86286 105.60571
## 2012 108.67286 109.27857 107.18143 106.11286 105.85571 104.94000 102.88571
## 2013 102.84000 102.26286 102.05857 102.64000 103.07714 103.23714 103.65286
## 2014 104.19714 104.23714 105.05714 105.02143 104.72857 103.44571 100.88000
## 2015 62.29143 58.92429 56.68429 55.77571 55.57286 54.35714 53.51857
## 2016 37.93429 37.78857 38.44286 39.52286 41.68000 43.68143 45.38857
## 2017 51.16714 51.25286 51.38286 50.67429 50.15286 49.95286 50.52714
## 2018 62.67000 65.31429 NA NA NA
## Aug Sep Oct Nov Dec
## 1993 NA NA NA 15.11143 14.70286
## 1994 16.86857 16.82000 16.78143 16.71571 16.76571
## 1995 16.85857 16.78000 16.84286 17.07429 17.49286
## 1996 20.83714 21.48857 22.16286 22.28143 22.12286
## 1997 18.87286 18.54714 18.13000 17.52571 16.72857
## 1998 12.94857 12.43143 12.28286 12.00429 12.10143
## 1999 20.07429 21.36571 22.64000 23.81571 24.84714
## 2000 30.00857 29.72429 29.20143 29.06714 28.46143
## 2001 24.25286 22.96286 21.84571 21.15714 20.84571
## 2002 26.14857 26.46571 27.36000 28.37857 28.89000
## 2003 28.18000 28.73857 29.23143 29.62286 30.19286
## 2004 40.52571 40.73714 41.79857 42.78857 44.05429
## 2005 56.41429 57.64286 58.86714 59.34286 59.20571
## 2006 65.62857 64.53000 62.42000 60.29286 58.69143
## 2007 75.30714 78.78714 82.00143 84.82857 89.35857
## 2008 103.98143 92.36857 79.84571 66.84714 57.14429
## 2009 69.08286 71.47286 72.61143 74.05286 75.14857
## 2010 77.59000 79.64571 82.21143 85.54429 90.23286
## 2011 104.05857 103.51000 103.68429 104.36571 106.83714
## 2012 101.10000 100.68571 102.73857 104.29429 103.90143
## 2013 104.19286 105.06571 105.40286 105.34143 104.75286
## 2014 96.89714 90.46714 81.71571 74.51000 67.76429
## 2015 51.45714 47.75143 43.24714 39.91714 38.72429
## 2016 46.03286 46.98714 47.83000 49.29000 50.15000
## 2017 51.63714 53.25143 56.11714 58.37429 60.40714
## 2018autoplot(PPDP, series= "Data") + autolayer(ma(PPDP, 7), series="7-MA") + xlab("Años") + ylab("Dólares") + ggtitle("Precio del barril de petróleo")## Warning: Removed 6 rows containing missing values (geom_path).
p3<-autoplot(PPDP, series= "Data") + autolayer(ma(PPDP, 7), series="7-MA") + xlab("Años") + ylab("Dólares") + ggtitle("Precio del barril de petróleo")MEDIA MOVIL DE ORDEN 9
ma(PPDP, order = 9)## Jan Feb Mar Apr May Jun Jul
## 1993
## 1994 14.92000 15.06111 15.21222 15.41222 15.68222 15.94222 16.30889
## 1995 17.15889 17.28778 17.24444 17.17667 17.27444 17.18778 17.11222
## 1996 18.01889 18.20667 18.59222 19.00333 19.49111 20.13667 20.65778
## 1997 21.34556 20.86333 20.27667 19.86667 19.31556 18.96111 18.81333
## 1998 16.07000 15.38333 14.56778 13.83444 13.46889 13.27556 13.02889
## 1999 12.96000 13.24778 13.88444 14.81667 16.18222 17.35000 18.84000
## 2000 24.95444 25.72333 26.41556 26.98222 27.75778 28.43444 29.00222
## 2001 28.05556 27.48778 26.75444 26.03111 26.03222 25.45111 24.50111
## 2002 21.89111 21.81111 22.36889 23.26778 24.35222 25.28333 25.79000
## 2003 28.20556 28.16556 28.28333 28.85444 28.74222 28.54889 28.13111
## 2004 31.40222 32.36444 33.35111 34.79111 36.08333 37.80667 39.00667
## 2005 45.20556 46.57111 47.62556 49.82111 52.33778 54.02889 55.16444
## 2006 61.23222 61.96556 63.55000 65.41333 66.04556 65.54000 65.36667
## 2007 60.11778 60.79222 62.53556 63.86778 65.62000 68.78111 72.53444
## 2008 95.21111 101.29556 106.94667 109.52778 110.65444 108.65444 104.27444
## 2009 56.49333 53.10333 52.21222 54.17444 57.17556 60.53333 64.50111
## 2010 76.20444 76.91333 76.96889 76.77778 76.91556 77.42667 78.51222
## 2011 95.10444 98.40778 101.31889 103.09222 104.29333 105.08889 105.92222
## 2012 107.29111 106.17000 105.82556 105.81000 106.03778 105.63111 104.35111
## 2013 102.83667 102.11000 102.31556 103.09222 103.93333 103.97000 103.41333
## 2014 104.87222 104.82889 104.80667 104.52000 103.45000 101.67000 98.57667
## 2015 66.04444 62.20667 58.68000 55.20222 53.60000 53.58333 52.28556
## 2016 39.75111 39.90778 39.59333 39.79000 40.73111 42.89889 44.48000
## 2017 50.34444 50.47000 50.28889 50.80889 50.84556 50.99333 51.61333
## 2018 62.78556 NA NA NA NA
## Aug Sep Oct Nov Dec
## 1993 NA NA NA NA 14.94222
## 1994 16.56667 16.76889 16.89556 16.91556 17.00111
## 1995 17.17000 17.07556 16.99556 17.22222 17.73111
## 1996 21.12667 21.41222 21.56333 21.65444 21.46444
## 1997 18.56444 18.24444 17.65889 17.12556 16.58889
## 1998 12.72778 12.49889 12.13444 12.21111 12.56333
## 1999 20.16556 21.21444 22.45444 23.68778 24.18556
## 2000 28.74778 29.02444 29.03000 28.51889 28.24556
## 2001 23.77889 23.04889 22.20778 21.83778 21.90778
## 2002 26.26222 26.85333 27.65444 28.30667 28.28556
## 2003 28.08778 28.73333 29.31889 29.95778 30.52667
## 2004 39.60333 40.63222 41.43889 43.15000 44.56667
## 2005 55.77444 57.08778 58.40667 59.18889 60.47778
## 2006 65.37333 63.76778 62.53222 61.67778 60.85667
## 2007 75.74778 78.59556 81.73222 85.47111 89.37778
## 2008 97.55222 90.34111 81.36444 71.93444 62.76222
## 2009 67.63778 70.62000 72.46556 73.59333 75.76111
## 2010 79.70222 80.64778 83.12444 86.89333 91.52222
## 2011 105.43111 104.41222 104.92556 106.25222 106.89111
## 2012 102.50667 101.55444 101.94889 103.25889 103.49222
## 2013 103.74222 104.10333 104.71000 105.18778 105.14444
## 2014 93.76111 87.34333 81.68556 75.51444 70.21556
## 2015 50.47889 47.39444 43.89667 41.23333 39.72333
## 2016 46.17778 47.60444 48.53889 48.89556 49.78778
## 2017 52.75667 54.32000 55.82778 57.82778 60.17556
## 2018autoplot(PPDP, series ="Data") + autolayer(ma(PPDP,9), series ="9-MA")+ xlab("Años") + ylab("Dólares") + ggtitle("Precio del barril de petróleo")## Warning: Removed 8 rows containing missing values (geom_path).
p4<-autoplot(PPDP, series ="Data") + autolayer(ma(PPDP,9), series ="9-MA")+ xlab("Años") + ylab("Dólares") + ggtitle("Precio del barril de petróleo")library(gridExtra)
grid.arrange (p1, p2, p3, p4)## Warning: Removed 2 rows containing missing values (geom_path).## Warning: Removed 4 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 8 rows containing missing values (geom_path).
DESCOMPOSICION CLASICA ADITIVA
fit <- decompose(PPDP, type = "additive")
autoplot(fit)
Se ve en la descomposición que los datos presentan estacionalidad y a lo largo de 1993 los precios del petroleo han tenido un incremento.
autoplot (PPDP, series ="Data") + autolayer(seasadj(fit), series = "Seasonally adj. data") + xlab("Años") + ylab("Dólares")+ ggtitle("Datos Estacionalmente Ajustados de Precios del barril de petróleo") 