How to build Exponential Smoothing Model - r

Question

My data :
library(forecast)
library(Mcomp)
# Time Series
# Subset the M3 data to contain the relevant series
ts.data<- subset(M3, 12)[[551]]
print(ts.data)
I have selected time series 551 of the monthly data of the M3 competition.
I want to build exponential smoothing model (ETS) and then calculate the in-sample error and out-of-sample error of the model.
How can i get through this ? Any help?
EDITED !
My code :
# Exponential Smoothing Model
library(forecast)
library(Mcomp)
# My data is
# Time Series
# Subset the M3 data to contain the relevant series
ts.data<- subset(M3, 12)[[551]]
print(ts.data)
library(tidyverse)
library(fpp2)
# Holt’s Method
# create training and validation
# of the data
data.train <- window(???)
data.test <- window(???)
holt.data <- holt(data.train,
h = 100)
autoplot(holt.data)
# holt's method
holt.data$model
# accuracy of the model
accuracy(holt.data, data.test)
# try to find the optimal value of beta through a loop ranging from 0.0001 to 0.5 that will minimize the RMSE test
# identify optimal alpha parameter
beta <- seq(.0001, .5, by = .001)
RMSE <- NA
for(i in seq_along(beta)) {
fit <- holt(data.train,
beta = beta[i],
h = 100)
RMSE[i] <- accuracy(fit,
data.test)[2,2]
}
# convert to a data frame and
# idenitify min alpha value
beta.fit <- data_frame(beta, RMSE)
beta.min <- filter(beta.fit,
RMSE == min(RMSE))
# plot RMSE vs. alpha
ggplot(beta.fit, aes(beta, RMSE)) +
geom_line() +
geom_point(data = beta.min,
aes(beta, RMSE),
size = 2, color = "red")
# Refit the model with the obtained optimal value of beta :
# Set the optimal value of beta nad also compare the predictive accuracy with our original model.
# new model with optimal beta
holt.data.opt <- holt(data.train,
h = 100,
beta = 0.0601)
# accuracy of first model
accuracy(holt.data, data.test)
# accuracy of new optimal model
accuracy(holt.data.opt, data.test)
p1 <- autoplot(holt.data) +
ggtitle("Original Holt's Model") +
coord_cartesian(ylim = c(400, 1000))
p2 <- autoplot(holt.data.opt) +
ggtitle("Optimal Holt's Model") +
coord_cartesian(ylim = c(400, 1000))
gridExtra::grid.arrange(p1, p2,
nrow = 1)
My problem is that i cant create my data.train and data.test samples.
data.train <- window(???)
data.test <- window(???)

Answer 1

From this good stack exchange answer you could do :
library(forecast)
library(Mcomp)
# Time Series
# Subset the M3 data to contain the relevant series
ts.data<- subset(M3, 12)[[551]]
ts.data
mod1 <- HoltWinters(ts.data$x, alpha=0.1, beta=FALSE, gamma=FALSE)
pred <- predict(mod1, n.ahead=30)
abs_error <- abs(pred - ts.data$xx)
mae <- sum(abs_error)/30
mae
# with forcats
mod2 <- forecast::ses(ts.data$x, h=30, alpha=0.1, initial="simple")
pred2 <- predict(mod2, n.ahead=30)
abs_error2 <- abs(pred2$mean - ts.data$xx)
mae2 <- sum(abs_error2)/30
mae2