series de tiempo-borrador
hector h
15/5/2020
factores que afectan la capacidad de prediccion
Que tambien entendemos los factores que contribuye a ello Cantidad de datos Disponibles Existe una variacion aleatoria natural / inexplcable El futuro es algo similar al pasado
Los pronosticos dependen en gran medida de los datos disponbiles
pronostico cuantitativo: -informacion numerica del pasado -patrones del pasado continuaran en el futuro
serie de tiempo -observaciones en linea de tiempo ej -precios diarios de acciones del IBM -lluvia mensual -resultado de ventas trimestrales para Amazon -ganancia anuales de google
todo lo que se observa secuencialmente en el tiempo es una serie de tiempo. por hora, dia,mes, año
ejemplo 1
Una empresa desaea pronosticos importante: el dato, que variables vamos a analizar y modelar equipo de trabajo ——————————————
ejemplo 2
el gobierno federal astraliano necesita pronosticar el presupuesto anual de de benerficios PBS, el PBS
PASOS PARA EL PRONOSTICO
DEFINICION DEL PROBLEMA variables,
RECOPILACION DE INFORMACION datos estadisticos interactuar con la experiencia de las personas
ANALISIS PRELIMINAR analisis exploratorio
ELECCION Y AJUSTE DE MODELOS modelo de regresion etc.
USO Y EVALUACION DE UN MODELO DE PRONOSTICO
es el pronostico una variable aleatoria
La respuesta es si nivel de confianza es mas alto , el intervalo aumenta
que se puede pronosticar
Ejemplo: Pronosticos de la demanda de energia Se cumplen las condiciones?
CASO 3 Cómo pronosticar el valor de reventa de los vehÃculos? ¿Cómo deberÃa afectar esto las polÃticas de arrendamiento y ventas? factores: modelo del vehiculo depreciacion pago de impuestos
CASO 4 DEPENDIENTE #TOTAL PASAJEROS PIB CAPITAL: PRECIO GAS CIUDAD TURISTICA CARGO POR COMBUSTIBLE
PRACTICA EN R clase 1
y=ts(c(123,39,78,52,110), start=2012)
y## Time Series:
## Start = 2012
## End = 2016
## Frequency = 1
## [1] 123 39 78 52 110y=ts(c(123,39,78,52,110), start=2012, frequency = 12)
y## Jan Feb Mar Apr May
## 2012 123 39 78 52 110plot(y)
plot.ts(rnorm(100))
library(fpp2) #esta libreria tiene muchos conjuntos de datos## Warning: package 'fpp2' was built under R version 3.6.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.6.3## Loading required package: forecast## Warning: package 'forecast' was built under R version 3.6.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Loading required package: fma## Warning: package 'fma' was built under R version 3.6.3## Loading required package: expsmooth## Warning: package 'expsmooth' was built under R version 3.6.3melsyd ## Time Series:
## Start = c(1987, 26)
## End = c(1992, 48)
## Frequency = 52
## First.Class Business.Class Economy.Class
## 1987.481 1.912 NA 20.167
## 1987.500 1.848 NA 20.161
## 1987.519 1.856 NA 19.993
## 1987.538 2.142 NA 20.986
## 1987.558 2.118 NA 20.497
## 1987.577 2.048 NA 20.770
## 1987.596 2.111 NA 21.111
## 1987.615 2.199 NA 20.675
## 1987.635 2.231 NA 22.092
## 1987.654 2.081 NA 20.772
## 1987.673 2.213 NA 21.642
## 1987.692 2.131 NA 21.911
## 1987.712 NA NA NA
## 1987.731 2.131 NA 23.777
## 1987.750 2.034 NA 22.658
## 1987.769 2.190 NA 23.515
## 1987.788 2.262 NA 21.384
## 1987.808 2.579 NA 24.344
## 1987.827 2.367 NA 21.137
## 1987.846 2.432 NA 23.069
## 1987.865 2.640 NA 23.664
## 1987.885 2.614 NA 23.219
## 1987.904 2.569 NA 23.192
## 1987.923 2.523 NA 23.475
## 1987.942 2.260 NA 22.377
## 1987.962 1.117 NA 16.606
## 1987.981 0.590 NA 13.987
## 1988.000 0.966 NA 16.251
## 1988.019 1.235 NA 18.439
## 1988.038 2.001 NA 20.262
## 1988.058 1.696 NA 19.535
## 1988.077 2.089 NA 22.467
## 1988.096 2.716 NA 24.559
## 1988.115 2.483 NA 24.591
## 1988.135 2.461 NA 24.511
## 1988.154 2.533 NA 24.524
## 1988.173 2.273 NA 23.119
## 1988.192 2.273 NA 23.106
## 1988.212 2.370 NA 23.292
## 1988.231 1.782 NA 21.566
## 1988.250 1.385 NA 18.565
## 1988.269 2.322 NA 24.361
## 1988.288 2.340 NA 22.983
## 1988.308 2.203 NA 21.062
## 1988.327 2.300 NA 21.766
## 1988.346 2.125 NA 22.285
## 1988.365 2.345 NA 22.867
## 1988.385 2.224 NA 23.276
## 1988.404 1.952 NA 20.924
## 1988.423 2.212 NA 21.716
## 1988.442 2.028 NA 20.908
## 1988.462 1.906 NA 21.410
## 1988.481 1.834 NA 21.361
## 1988.500 1.797 NA 22.412
## 1988.519 1.869 NA 21.290
## 1988.538 1.886 NA 22.247
## 1988.558 2.131 NA 22.445
## 1988.577 2.021 NA 21.260
## 1988.596 2.199 NA 22.771
## 1988.615 2.140 NA 23.723
## 1988.635 2.190 NA 23.757
## 1988.654 1.917 NA 23.482
## 1988.673 2.096 NA 23.653
## 1988.692 2.254 NA 26.054
## 1988.712 2.251 NA 25.695
## 1988.731 2.072 NA 26.720
## 1988.750 0.993 NA 15.033
## 1988.769 1.675 NA 22.340
## 1988.788 1.463 NA 19.297
## 1988.808 2.256 NA 23.761
## 1988.827 2.216 NA 22.150
## 1988.846 2.218 NA 22.236
## 1988.865 2.568 NA 23.157
## 1988.885 2.483 NA 24.387
## 1988.904 2.545 NA 23.844
## 1988.923 2.599 NA 23.681
## 1988.942 2.483 NA 24.456
## 1988.962 1.793 NA 19.899
## 1988.981 0.516 NA 13.662
## 1989.000 0.873 NA 15.698
## 1989.019 1.195 NA 18.189
## 1989.038 1.525 NA 19.448
## 1989.058 1.906 NA 20.891
## 1989.077 2.246 NA 22.284
## 1989.096 2.247 NA 23.247
## 1989.115 2.316 NA 24.440
## 1989.135 2.276 NA 22.519
## 1989.154 2.403 NA 22.818
## 1989.173 2.432 NA 23.472
## 1989.192 2.490 NA 24.117
## 1989.212 2.013 NA 24.435
## 1989.231 1.508 NA 20.589
## 1989.250 2.251 NA 23.427
## 1989.269 2.079 NA 20.923
## 1989.288 2.210 NA 22.959
## 1989.308 1.960 NA 19.152
## 1989.327 2.251 NA 22.011
## 1989.346 2.013 NA 20.228
## 1989.365 2.252 NA 21.332
## 1989.385 2.067 NA 20.500
## 1989.404 1.831 NA 19.090
## 1989.423 2.069 NA 21.692
## 1989.442 1.953 NA 20.157
## 1989.462 2.199 NA 22.231
## 1989.481 1.771 NA 20.914
## 1989.500 1.696 NA 21.454
## 1989.519 1.788 NA 21.345
## 1989.538 1.636 1.524 19.260
## 1989.558 1.741 2.212 18.781
## 1989.577 1.597 1.777 17.445
## 1989.596 1.943 2.552 19.628
## 1989.615 1.660 1.889 17.692
## 1989.635 0.616 0.851 7.046
## 1989.654 0.000 0.000 0.000
## 1989.673 0.000 0.000 0.000
## 1989.692 0.000 0.000 0.000
## 1989.712 0.000 0.000 0.000
## 1989.731 0.000 0.000 0.000
## 1989.750 0.000 0.000 0.000
## 1989.769 0.000 0.000 0.000
## 1989.788 0.053 0.618 11.569
## 1989.808 0.040 0.565 11.973
## 1989.827 0.354 0.414 11.123
## 1989.846 0.505 0.543 11.479
## 1989.865 0.711 0.712 16.969
## 1989.885 0.723 0.652 15.997
## 1989.904 0.796 0.709 16.555
## 1989.923 0.856 0.793 17.959
## 1989.942 0.845 0.838 18.868
## 1989.962 0.807 0.763 15.400
## 1989.981 0.276 0.266 10.544
## 1990.000 0.339 0.362 12.755
## 1990.019 0.581 0.665 19.020
## 1990.038 0.894 0.957 20.077
## 1990.058 0.936 1.145 22.124
## 1990.077 1.111 1.312 19.920
## 1990.096 1.446 1.689 17.215
## 1990.115 1.517 1.762 17.662
## 1990.135 1.513 2.160 19.828
## 1990.154 1.508 1.877 18.754
## 1990.173 1.636 2.048 20.390
## 1990.192 1.380 1.824 18.383
## 1990.212 1.453 1.986 19.377
## 1990.231 1.459 2.032 20.133
## 1990.250 1.538 1.956 19.210
## 1990.269 1.327 1.503 20.736
## 1990.288 1.027 1.256 18.821
## 1990.308 1.365 1.752 20.952
## 1990.327 1.475 1.878 20.565
## 1990.346 1.365 1.945 20.311
## 1990.365 1.351 1.735 17.859
## 1990.385 1.231 1.825 19.385
## 1990.404 1.447 1.715 17.962
## 1990.423 1.408 1.594 19.569
## 1990.442 1.089 1.377 17.894
## 1990.462 1.357 1.686 18.114
## 1990.481 1.287 1.560 19.170
## 1990.500 1.087 1.692 18.713
## 1990.519 1.017 1.597 20.520
## 1990.538 1.090 1.729 20.345
## 1990.558 1.343 1.733 20.015
## 1990.577 1.164 1.813 18.027
## 1990.596 1.169 1.725 19.697
## 1990.615 1.465 1.909 19.897
## 1990.635 1.242 1.765 19.213
## 1990.654 1.220 1.781 19.773
## 1990.673 1.231 1.717 20.128
## 1990.692 1.266 1.884 21.181
## 1990.712 1.337 1.881 21.933
## 1990.731 1.079 1.433 20.049
## 1990.750 1.223 1.534 23.358
## 1990.769 1.351 1.926 21.120
## 1990.788 1.269 1.870 21.970
## 1990.808 1.382 1.942 21.411
## 1990.827 1.435 2.222 22.569
## 1990.846 1.371 1.909 20.849
## 1990.865 1.341 2.089 20.658
## 1990.885 1.399 2.026 21.192
## 1990.904 1.440 2.009 21.502
## 1990.923 1.236 2.034 22.152
## 1990.942 1.441 2.085 21.904
## 1990.962 0.988 1.330 20.634
## 1990.981 0.300 0.318 15.535
## 1991.000 0.401 0.522 16.690
## 1991.019 0.529 0.824 19.150
## 1991.038 0.786 1.113 21.128
## 1991.058 1.086 1.285 21.136
## 1991.077 0.725 1.107 19.693
## 1991.096 1.127 1.591 21.576
## 1991.115 1.146 1.726 21.449
## 1991.135 1.086 1.758 22.298
## 1991.154 1.134 1.883 21.056
## 1991.173 1.020 2.089 19.014
## 1991.192 1.022 2.198 19.511
## 1991.212 1.224 2.449 20.162
## 1991.231 0.895 1.739 21.158
## 1991.250 0.658 1.480 19.059
## 1991.269 0.977 2.152 21.972
## 1991.288 0.916 2.258 22.877
## 1991.308 0.830 1.776 22.190
## 1991.327 0.990 2.355 22.533
## 1991.346 0.770 2.140 20.682
## 1991.365 0.801 1.988 21.788
## 1991.385 0.839 2.083 21.299
## 1991.404 0.835 2.115 20.663
## 1991.423 0.802 1.884 21.948
## 1991.442 0.763 1.797 21.009
## 1991.462 0.770 1.944 20.443
## 1991.481 0.838 2.001 21.418
## 1991.500 0.742 1.668 23.273
## 1991.519 0.793 1.527 25.763
## 1991.538 0.900 1.477 26.045
## 1991.558 0.960 1.949 23.831
## 1991.577 0.800 1.914 22.742
## 1991.596 0.807 1.632 22.962
## 1991.615 0.841 1.796 25.253
## 1991.635 0.880 1.696 25.239
## 1991.654 0.818 1.718 27.387
## 1991.673 0.817 1.442 26.824
## 1991.692 0.819 1.713 27.294
## 1991.712 0.998 1.796 28.935
## 1991.731 1.220 1.860 31.642
## 1991.750 0.966 1.554 32.468
## 1991.769 0.895 1.623 27.673
## 1991.788 0.978 1.641 28.890
## 1991.808 0.913 1.818 26.465
## 1991.827 0.947 1.969 28.296
## 1991.846 1.002 1.886 29.274
## 1991.865 1.081 2.030 30.686
## 1991.885 0.977 1.883 29.786
## 1991.904 1.027 1.871 31.155
## 1991.923 0.895 1.910 28.459
## 1991.942 0.900 1.921 27.195
## 1991.962 0.762 1.672 26.274
## 1991.981 0.329 0.386 25.204
## 1992.000 0.351 0.446 24.434
## 1992.019 0.419 0.819 27.323
## 1992.038 0.618 1.238 27.303
## 1992.058 0.845 1.761 30.334
## 1992.077 0.727 1.650 26.833
## 1992.096 1.200 2.031 25.811
## 1992.115 1.801 2.064 27.238
## 1992.135 1.727 2.418 28.788
## 1992.154 1.992 2.171 27.263
## 1992.173 1.865 2.362 27.217
## 1992.192 1.801 2.328 26.410
## 1992.212 1.661 2.336 26.118
## 1992.231 2.366 10.301 18.642
## 1992.250 2.003 9.964 16.518
## 1992.269 2.092 10.433 17.276
## 1992.288 1.703 8.281 21.662
## 1992.308 1.337 6.128 20.473
## 1992.327 1.985 9.709 18.336
## 1992.346 1.808 8.828 17.018
## 1992.365 1.839 8.078 18.111
## 1992.385 1.714 7.527 18.410
## 1992.404 1.730 7.486 20.541
## 1992.423 1.725 6.711 21.408
## 1992.442 1.456 5.930 21.545
## 1992.462 1.447 5.462 21.732
## 1992.481 1.357 3.710 26.173
## 1992.500 1.280 2.894 27.432
## 1992.519 1.363 3.008 28.362
## 1992.538 1.228 2.829 29.827
## 1992.558 1.411 3.252 29.870
## 1992.577 1.130 3.021 26.534
## 1992.596 1.153 2.667 26.434
## 1992.615 1.257 2.740 26.137
## 1992.635 1.259 2.807 27.365
## 1992.654 1.153 2.961 27.910
## 1992.673 1.202 2.570 26.311
## 1992.692 1.185 2.671 27.538
## 1992.712 1.247 2.809 29.445
## 1992.731 1.434 2.712 28.326
## 1992.750 1.450 2.606 30.203
## 1992.769 1.227 2.500 27.838
## 1992.788 1.245 2.898 27.760
## 1992.808 1.417 3.152 27.322
## 1992.827 1.458 3.053 28.837
## 1992.846 1.398 2.745 26.548
## 1992.865 1.423 3.156 27.279
## 1992.885 1.358 3.069 27.306
## 1992.904 1.488 3.379 28.299#?melsyd
plot(melsyd)
#solo clase economica
plot(melsyd[, "Economy.Class"])
autoplot(melsyd[,"Economy.Class"]) +ggtitle("Economy class passengers: Melbourne-Sydney") +xlab("Year") +ylab("Thousands")
autoplot(a10) +ggtitle("Antidiabetic drug sales") +ylab("$ million") +xlab("Year")
tendencia clara y creciento
Se observa un comportamiento estacional, se repite a menudo a traves de la historia, la amplitud del pido va aumentando en la serie
Unemployment benefits in Australia
autoplot(dole) +ggtitle("Unemployment benefits in Australia") +ylab("$ million") +xlab("Year")
Annual Canadian Lynx trappings 1821–1934
autoplot(lynx) +ggtitle("Annual Canadian Lynx trappings 1821–1934") +ylab("$ million") +xlab("Year")
autoplot(lynx) +ggtitle("Daily closing stock prices of Google Inc") +ylab("$ million") +xlab("Year")