Auto_ARIMA_Forecast.R

data("AirPassengers")
library(forecast)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: timeDate

## This is forecast 7.3

plot(AirPassengers)

AirPassengers=ts(AirPassengers,frequency=12, start=c(1949,1))
plot.ts(AirPassengers)

library(TTR)
AirPassengerscomponents <- decompose(AirPassengers)
plot(AirPassengerscomponents)

AirPassengerscomponents$seasonal

##             Jan        Feb        Mar        Apr        May        Jun
## 1949 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1950 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1951 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1952 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1953 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1954 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1955 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1956 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1957 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1958 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1959 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
## 1960 -24.748737 -36.188131  -2.241162  -8.036616  -4.506313  35.402778
##             Jul        Aug        Sep        Oct        Nov        Dec
## 1949  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1950  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1951  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1952  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1953  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1954  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1955  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1956  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1957  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1958  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1959  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949
## 1960  63.830808  62.823232  16.520202 -20.642677 -53.593434 -28.619949

AirPassengersseasonallyadjusted <- AirPassengers - AirPassengerscomponents$seasonal
plot(AirPassengersseasonallyadjusted)

fit3 <- HoltWinters(AirPassengersseasonallyadjusted, beta=FALSE, gamma=FALSE)
fit4<- ets(AirPassengers)
forecast(fit3, 3)

##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Jan 1961       460.6188 438.5155 482.7222 426.8147 494.4230
## Feb 1961       460.6188 429.3610 491.8767 412.8141 508.4235
## Mar 1961       460.6188 422.3364 498.9013 402.0709 519.1668

plot(forecast(fit3, 3))

forecast(fit4, 3)

##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Jan 1961       441.7479 420.9284 462.5675 409.9071 473.5887
## Feb 1961       433.0931 407.5924 458.5938 394.0931 472.0931
## Mar 1961       496.6067 462.3205 530.8930 444.1705 549.0430

plot(forecast(fit4, 3))