A01720802_Actividad2
Lorena Villarreal
2024-02-16
Concepto
Una serie de tiempo es una colección de observaciones sobre un determinado fenómico, efectuadas en momentos sucesivos, usualmente equiespaciados.
Algunos ejemplos de series de tiempo son:
1. Precios de acciones.
2. Niveles de inventario.
3. Rotación de personal.
4. Ventas.
5. PIB (GDP).
Más información:
Libro R for Data
Science (2e).
Instalar paquetes y llamar librerías
Crear la serie de tiempo.
Ejemplo:
Los siguientes datos de producción trimestral inician en el primer
trimestre del 2020.
Se busca pronosticar la producción de los siguientes 5 trimestres.
produccion <- c(50, 53, 55, 57, 55, 60)
ts <- ts(data = produccion, start = c(2020,1), frequency = 4)
ts## Qtr1 Qtr2 Qtr3 Qtr4
## 2020 50 53 55 57
## 2021 55 60Crear el modelo ARIMA
ARIMA significa AutoRegressive Integrated Moving Average o Modelo Autoregresivo Integrado de Promedio Móvil.
arima <- auto.arima(ts, D=1)
arima## Series: ts
## ARIMA(0,0,0)(0,1,0)[4] with drift
##
## Coefficients:
## drift
## 1.5000
## s.e. 0.1768
##
## sigma^2 = 2.01: log likelihood = -2.84
## AIC=9.68 AICc=-2.32 BIC=7.06summary(arima)## Series: ts
## ARIMA(0,0,0)(0,1,0)[4] with drift
##
## Coefficients:
## drift
## 1.5000
## s.e. 0.1768
##
## sigma^2 = 2.01: log likelihood = -2.84
## AIC=9.68 AICc=-2.32 BIC=7.06
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.03333332 0.5787923 0.3666667 0.03685269 0.6429133 0.06111111
## ACF1
## Training set -0.5073047Realizar pronóstico
pronostico <- forecast(arima, level= c(95), h=5)
pronostico## Point Forecast Lo 95 Hi 95
## 2021 Q3 61 58.22127 63.77873
## 2021 Q4 63 60.22127 65.77873
## 2022 Q1 61 58.22127 63.77873
## 2022 Q2 66 63.22127 68.77873
## 2022 Q3 67 63.07028 70.92972plot(pronostico)
Actividad 2: Hershey’s
Cargar base de datos
library(readxl)
lechita <- read_excel("Desktop/Primer Bloque/Modulo 1/Ventas_Históricas_Lechitas.xlsx")
lechita$Ventas <- as.numeric(lechita$Ventas)Crear una serie de tiempo
ts1 <- ts(data = lechita$Ventas, start = c(2017,1), frequency = 12)
ts1## Jan Feb Mar Apr May Jun Jul Aug
## 2017 25520.51 23740.11 26253.58 25868.43 27072.87 27150.50 27067.10 28145.25
## 2018 28463.69 26996.11 29768.20 29292.51 29950.68 30099.17 30851.26 32271.76
## 2019 32496.44 31287.28 33376.02 32949.77 34004.11 33757.89 32927.30 34324.12
## Sep Oct Nov Dec
## 2017 27546.29 28400.37 27441.98 27852.47
## 2018 31940.74 32995.93 32197.12 31984.82
## 2019 35151.28 36133.07 34799.91 34846.17Crear el modelo arima
arima1 <- auto.arima(ts1, D=1)
arima1## Series: ts1
## ARIMA(1,0,0)(1,1,0)[12] with drift
##
## Coefficients:
## ar1 sar1 drift
## 0.6383 -0.5517 288.8979
## s.e. 0.1551 0.2047 14.5026
##
## sigma^2 = 202701: log likelihood = -181.5
## AIC=371 AICc=373.11 BIC=375.72summary(arima1)## Series: ts1
## ARIMA(1,0,0)(1,1,0)[12] with drift
##
## Coefficients:
## ar1 sar1 drift
## 0.6383 -0.5517 288.8979
## s.e. 0.1551 0.2047 14.5026
##
## sigma^2 = 202701: log likelihood = -181.5
## AIC=371 AICc=373.11 BIC=375.72
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 25.22158 343.864 227.17 0.08059932 0.7069542 0.06491044 0.2081026Realizar pronóstico
pronostico1 <- forecast(arima1, level= c(95), h=12)
pronostico1## Point Forecast Lo 95 Hi 95
## Jan 2020 35498.90 34616.48 36381.32
## Feb 2020 34202.17 33155.28 35249.05
## Mar 2020 36703.01 35596.10 37809.92
## Apr 2020 36271.90 35141.44 37402.36
## May 2020 37121.98 35982.07 38261.90
## Jun 2020 37102.65 35958.90 38246.40
## Jul 2020 37151.04 36005.73 38296.34
## Aug 2020 38564.64 37418.70 39710.58
## Sep 2020 38755.22 37609.03 39901.42
## Oct 2020 39779.02 38632.72 40925.32
## Nov 2020 38741.63 37595.28 39887.97
## Dec 2020 38645.86 37499.50 39792.22plot(pronostico1)
Finanzas Corporativas
Instalar paquetes y llamar librerias
#install.packages("finreportr")
library(finreportr)Información disponible
Con la función “FinReportR” podemos obtener la siguiente
información:
- CompanyInfo() = Brinda información general como nombre, ubicación,
ZIP, etc.
- AnnualReports() = Brinda el nombre, fecha, y número de acceso.
- GetIncome() = Estado de resultados.
- GetBalanceSheet() = Estado de resultados.
- GetCashFlow() = Brinda el flujo de efectivo.
options(HTTPUserAgent = "a a@gmail.com")
CompanyInfo("JPM")## company CIK SIC state state.inc FY.end street.address
## 1 JPMORGAN CHASE & CO 0000019617 6021 NY DE 1231 383 MADISON AVENUE
## city.state
## 1 NEW YORK NY 10017AnnualReports("BABA", foreign = TRUE)## filing.name filing.date accession.no
## 1 20-F/A 2024-02-23 0001193125-24-044480
## 2 20-F 2023-07-21 0000950170-23-033752
## 3 20-F 2022-07-26 0001104659-22-082622
## 4 20-F 2021-07-27 0001104659-21-096092
## 5 20-F 2020-07-09 0001104659-20-082409
## 6 20-F 2019-06-05 0001047469-19-003492
## 7 20-F 2018-07-27 0001047469-18-005257
## 8 20-F 2017-06-15 0001047469-17-004019
## 9 20-F 2016-05-24 0001047469-16-013400
## 10 20-F 2015-06-25 0001047469-15-005768# google_income <- GetIncome("GOOG", 2016)
amazon_balance <- GetBalanceSheet("AMZN", 2015)
apple_cash <- GetCashFlow("AAPL", 2014)options(HTTPUserAgent = "a a@gmail.com")
CompanyInfo("JPM")## company CIK SIC state state.inc FY.end street.address
## 1 JPMORGAN CHASE & CO 0000019617 6021 NY DE 1231 383 MADISON AVENUE
## city.state
## 1 NEW YORK NY 10017AnnualReports("BABA",foreign=TRUE)## filing.name filing.date accession.no
## 1 20-F/A 2024-02-23 0001193125-24-044480
## 2 20-F 2023-07-21 0000950170-23-033752
## 3 20-F 2022-07-26 0001104659-22-082622
## 4 20-F 2021-07-27 0001104659-21-096092
## 5 20-F 2020-07-09 0001104659-20-082409
## 6 20-F 2019-06-05 0001047469-19-003492
## 7 20-F 2018-07-27 0001047469-18-005257
## 8 20-F 2017-06-15 0001047469-17-004019
## 9 20-F 2016-05-24 0001047469-16-013400
## 10 20-F 2015-06-25 0001047469-15-005768google_income<-GetIncome("GOOG",2016)
amazon_balance <- GetBalanceSheet("AMZN",2015)
apple_cash<-GetCashFlow("AAPL",2014)Conseguir los datos
options(HTTPUserAgent = "a a@gmail.com")
jpm_income_2015<-GetIncome("JPM",2015)
jpm_balance_2015 <- GetBalanceSheet("JPM",2015)
jpm_income_2018<-GetIncome("JPM",2018)
jpm_balance_2018 <- GetBalanceSheet("JPM",2018)
jpm_balance_2013<-GetBalanceSheet("JPM",2013)
jpm_balance_2016<-GetBalanceSheet("JPM",2016)Manipulación de Datos
conjunto<-rbind(jpm_balance_2015,jpm_balance_2018,jpm_income_2015,jpm_income_2018,jpm_balance_2013,jpm_balance_2016)
conjunto<-conjunto%>%
filter(Metric %in% c("Assets","Net Income (Loss) Attributable to Parent","Liabilities","Deposits"))conjunto$endDate <- as.Date(conjunto$endDate)
conjunto_filtered <- conjunto %>%
filter(endDate >= as.Date("2012-01-01") & endDate <= as.Date("2017-12-31")) %>%
group_by(Metric, year = as.numeric(format(endDate, "%Y"))) %>%
mutate(row_number = row_number()) %>%
filter(row_number == 1) %>%
select(-row_number) %>%
ungroup()conjunto_filtered<-conjunto_filtered%>%select(1,3,6)df_pivot <- conjunto_filtered %>%
spread(key = Metric, value = Amount)
# Opcional: Renombramos las columnas para que tengan nombres más descriptivos
names(df_pivot)[1] <- "Year"
df_pivot$Assets<-as.numeric(df_pivot$Assets)
df_pivot$Deposits<-as.numeric(df_pivot$Deposits)
df_pivot$Liabilities<-as.numeric(df_pivot$Liabilities)
df_pivot$`Net Income (Loss) Attributable to Parent`<-as.numeric(df_pivot$`Net Income (Loss) Attributable to Parent`)Crear serie de tiempo
ts2<-ts(df_pivot$Deposits,start =c(2012,1),frequency =1)
ts2## Time Series:
## Start = 2012
## End = 2017
## Frequency = 1
## [1] 1.193593e+12 1.287765e+12 1.363427e+12 1.279715e+12 1.375179e+12
## [6] 1.443982e+12Crear Modelo ARIMA
ARIMA significa AutoRegressive Integrated Moving Average o Modelo Autorregresivo Integrado de Promedio Movil
arima3<-auto.arima(ts2,d=1)
summary(arima3)## Series: ts2
## ARIMA(0,1,0)
##
## sigma^2 = 7.09e+21: log likelihood = -132.88
## AIC=267.75 AICc=269.09 BIC=267.36
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 41930432067 76865093443 69834432067 3.021252 5.201737 0.835714
## ACF1
## Training set -0.3972446Generar el pronóstico
pronostico3 <- forecast(arima3,level=c(95), h=5)
pronostico3## Point Forecast Lo 95 Hi 95
## 2018 1.443982e+12 1.278950e+12 1.609014e+12
## 2019 1.443982e+12 1.210592e+12 1.677372e+12
## 2020 1.443982e+12 1.158138e+12 1.729826e+12
## 2021 1.443982e+12 1.113918e+12 1.774046e+12
## 2022 1.443982e+12 1.074959e+12 1.813005e+12plot(pronostico3)