install.packages(‘forecast’)
library(forecast)
- Load the Air Passengers’ Dataset and View Its Class
data(“AirPassengers”)
class(AirPassengers
Here, ts represents that it’s a time series dataset.
- Display the Dataset
Let’s check on our date values
start(AirPassengers)
[1] 1949 1
end(AirPassengers)
[1] 1960 12
So, our start date is January 1949, while the end date is December 1960.
- Find out If There Are Any Missing Values
sum(is.na(AirPassengers))
[1] 0
- Check the Summary of the Dataset
summary(AirPassengers)
- Plot the Dataset
plot(AirPassengers)
- Decompose the Data Into Four Components
tsdata <- ts(AirPassengers, frequency = 12)
ddata <- decompose(tsdata, “multiplicative”)
plot(ddata)
- Plot the Different Components Individually
plot(ddata$trend)
plot(ddata$seasonal)
plot(ddata$random)
- Plot a Trendline on the Original Dataset
plot(AirPassengers)
abline(reg=lm(AirPassengers~time(AirPassengers)))
