Introduction

This R is the assignment of week 2 discussion for the course of Predictive Analytics. The task is : Pick a time series that has monthly data and conduct additive and multiplicative decomposition. (You cannot decompose daily data!) Which one worked better? How can you tell? How would you use the results in forecasting (or would you?)?

Descriptive

The purpose of this section is to preprocess and describe the dataset. The dataset used was the Monthly Sunspot Dataset, This dataset describes a monthly count of the number of observed sunspots for just over 230 years (1749-1983). Here is a first view of the data.

data <- read.csv("https://raw.githubusercontent.com/jbrownlee/Datasets/master/monthly-sunspots.csv")
summary(data)
##     Month              Sunspots     
##  Length:2820        Min.   :  0.00  
##  Class :character   1st Qu.: 15.70  
##  Mode  :character   Median : 42.00  
##                     Mean   : 51.27  
##                     3rd Qu.: 74.92  
##                     Max.   :253.80

Inicially we plot the time serie.

Additive Decomposition

The additive approach is based in

dec1<-decompose(data,type="additive")  #build an additive decomposition model

plot(dec1)

Multiplicative Decomposition

The additive approach is based in

dec2<-decompose(data,type="multiplicative")  #build a multiplicative decomposition model

plot(dec2)

STL decomposition

#Decompose the time series 
data2 <- stl(data, s.window="periodic", robust=TRUE)
plot(data2)

X11 decomposition

fit <- seas(x = data, x11 = "")
autoplot(fit)