# UNIVERSIDAD NACIONAL DEL ALTIPLANO
# INGENIERIA ESTADISTICA E INFORMATICA
# CURSO: SERIES DE TIEMPO
# EJEMPLO
library(readxl)
## Warning: package 'readxl' was built under R version 4.0.2
Petroleo <- read_excel("E:\\SERIES DE TIEMPO\\TAREA 01\\Petroleo.xlsx")
# View(petroleo1)
attach(Petroleo)
names(Petroleo)
## [1] "Precio" "Exportaciones"
# Cambiar nombre de la BD
petroleo <- Petroleo
petroleo
## # A tibble: 87 x 2
## Precio Exportaciones
## <dbl> <dbl>
## 1 98.0 981.
## 2 103. 1017.
## 3 101. 893.
## 4 97.8 1057.
## 5 97.9 915.
## 6 97.0 944.
## 7 99.7 1034.
## 8 99.2 978.
## 9 98.1 1007.
## 10 93.9 1012.
## # ... with 77 more rows
# Serie de Tiempo
petroleo.ts<- ts(petroleo,start = 2013,frequency = 12)
petroleo.ts
## Precio Exportaciones
## Jan 2013 97.98278 980.8128
## Feb 2013 103.35294 1016.6572
## Mar 2013 100.77090 893.4956
## Apr 2013 97.75538 1057.0875
## May 2013 97.90686 914.6077
## Jun 2013 96.99315 943.7099
## Jul 2013 99.67255 1033.8571
## Aug 2013 99.24414 977.7406
## Sep 2013 98.14572 1007.0700
## Oct 2013 93.87329 1011.7472
## Nov 2013 88.48590 984.7709
## Dec 2013 89.40966 1031.8087
## Jan 2014 88.31081 893.0505
## Feb 2014 90.82224 1005.5936
## Mar 2014 91.39522 890.6116
## Apr 2014 93.88863 858.8093
## May 2014 95.08631 900.7373
## Jun 2014 97.01105 877.4955
## Jul 2014 93.32017 863.5452
## Aug 2014 89.50745 946.0628
## Sep 2014 84.12824 949.0946
## Oct 2014 72.50936 882.7212
## Nov 2014 63.47980 934.6265
## Dec 2014 50.18138 1012.5247
## Jan 2015 40.45127 970.9621
## Feb 2015 45.98606 930.4431
## Mar 2015 46.01733 871.8023
## Apr 2015 49.32555 750.1818
## May 2015 52.03910 748.3895
## Jun 2015 52.82573 735.7843
## Jul 2015 45.16595 879.7783
## Aug 2015 38.41927 931.2851
## Sep 2015 36.29036 895.4856
## Oct 2015 34.86077 899.8572
## Nov 2015 32.50883 857.3915
## Dec 2015 26.19610 780.8092
## Jan 2016 23.10238 796.7651
## Feb 2016 23.72933 886.6428
## Mar 2016 28.32656 774.4293
## Apr 2016 31.33582 853.7166
## May 2016 36.72636 980.1193
## Jun 2016 39.80715 967.6453
## Jul 2016 37.97153 904.3905
## Aug 2016 37.74090 973.4510
## Sep 2016 36.82666 1052.1847
## Oct 2016 40.66836 1046.4826
## Nov 2016 38.36162 993.6913
## Dec 2016 42.32596 975.4781
## Jan 2017 44.50008 936.8024
## Feb 2017 44.17497 1088.6730
## Mar 2017 41.93243 965.0702
## Apr 2017 43.22118 976.3228
## May 2017 43.85074 950.1525
## Jun 2017 41.15105 1046.2386
## Jul 2017 43.87771 1117.7205
## Aug 2017 45.55484 967.4410
## Sep 2017 48.16237 1000.4794
## Oct 2017 48.89114 1195.7028
## Nov 2017 53.34505 1264.6378
## Dec 2017 54.05576 1322.8053
## Jan 2018 57.45399 1051.3040
## Feb 2018 56.15676 1354.4276
## Mar 2018 57.22458 1136.2008
## Apr 2018 58.15973 1114.5576
## May 2018 62.89929 1189.2718
## Jun 2018 64.63686 1109.7930
## Jul 2018 66.42162 1155.8421
## Aug 2018 64.26567 1181.0645
## Sep 2018 68.36348 1205.8530
## Oct 2018 71.15330 1026.6360
## Nov 2018 59.82583 1134.6351
## Dec 2018 51.86867 1198.1957
## Jan 2019 54.05666 1071.4520
## Feb 2019 57.37981 1475.2783
## Mar 2019 59.46449 1150.2987
## Apr 2019 62.07703 1023.4217
## May 2019 60.33674 1204.6957
## Jun 2019 56.90537 995.0349
## Jul 2019 57.87580 1078.6825
## Aug 2019 49.58496 1081.3750
## Sep 2019 55.05522 994.5735
## Oct 2019 51.00463 963.0636
## Nov 2019 50.68969 1114.4386
## Dec 2019 54.54901 1066.4297
## Jan 2020 49.79299 1212.2405
## Feb 2020 44.63505 990.4275
## Mar 2020 28.92302 1079.1894
# Graficar la BD de petroleo
plot(petroleo.ts)
# Grafico para la BD de Precio
precio.ts <- ts(petroleo.ts[,1],start = 2013,frequency = 12)
precio.ts
## Jan Feb Mar Apr May Jun Jul
## 2013 97.98278 103.35294 100.77090 97.75538 97.90686 96.99315 99.67255
## 2014 88.31081 90.82224 91.39522 93.88863 95.08631 97.01105 93.32017
## 2015 40.45127 45.98606 46.01733 49.32555 52.03910 52.82573 45.16595
## 2016 23.10238 23.72933 28.32656 31.33582 36.72636 39.80715 37.97153
## 2017 44.50008 44.17497 41.93243 43.22118 43.85074 41.15105 43.87771
## 2018 57.45399 56.15676 57.22458 58.15973 62.89929 64.63686 66.42162
## 2019 54.05666 57.37981 59.46449 62.07703 60.33674 56.90537 57.87580
## 2020 49.79299 44.63505 28.92302
## Aug Sep Oct Nov Dec
## 2013 99.24414 98.14572 93.87329 88.48590 89.40966
## 2014 89.50745 84.12824 72.50936 63.47980 50.18138
## 2015 38.41927 36.29036 34.86077 32.50883 26.19610
## 2016 37.74090 36.82666 40.66836 38.36162 42.32596
## 2017 45.55484 48.16237 48.89114 53.34505 54.05576
## 2018 64.26567 68.36348 71.15330 59.82583 51.86867
## 2019 49.58496 55.05522 51.00463 50.68969 54.54901
## 2020
plot(precio.ts)
# Grafico para la BD de Exportaciones
Export.ts <- ts(petroleo.ts[,2],start = 2013,frequency = 12)
Export.ts
## Jan Feb Mar Apr May Jun Jul
## 2013 980.8128 1016.6572 893.4956 1057.0875 914.6077 943.7099 1033.8571
## 2014 893.0505 1005.5936 890.6116 858.8093 900.7373 877.4955 863.5452
## 2015 970.9621 930.4431 871.8023 750.1818 748.3895 735.7843 879.7783
## 2016 796.7651 886.6428 774.4293 853.7166 980.1193 967.6453 904.3905
## 2017 936.8024 1088.6730 965.0702 976.3228 950.1525 1046.2386 1117.7205
## 2018 1051.3040 1354.4276 1136.2008 1114.5576 1189.2718 1109.7930 1155.8421
## 2019 1071.4520 1475.2783 1150.2987 1023.4217 1204.6957 995.0349 1078.6825
## 2020 1212.2405 990.4275 1079.1894
## Aug Sep Oct Nov Dec
## 2013 977.7406 1007.0700 1011.7472 984.7709 1031.8087
## 2014 946.0628 949.0946 882.7212 934.6265 1012.5247
## 2015 931.2851 895.4856 899.8572 857.3915 780.8092
## 2016 973.4510 1052.1847 1046.4826 993.6913 975.4781
## 2017 967.4410 1000.4794 1195.7028 1264.6378 1322.8053
## 2018 1181.0645 1205.8530 1026.6360 1134.6351 1198.1957
## 2019 1081.3750 994.5735 963.0636 1114.4386 1066.4297
## 2020
plot(Export.ts)
