##libraries needed## library(forecast) library(ggplot2)
##times series plot## beerTS=ts(beer$`Monthly beer production`) {r,echo=false} autoplot(beerTS)
##decompisition Additive## beerTS=ts(beer$`Monthly beer production`,frequency = 12) decompADD=decompose(beerTS,type="additive") plot(decompADD)
##decomposition Multiplicative## decompMult=decompose(beerTS,type = "multiplicative") plot(decompMult)
##Periodic## period=stl(beerTS,“periodic”) > summary(period) Call: stl(x = beerTS, s.window = “periodic”)
Time.series components: seasonal trend remainder
Min. :-7.030195 Min. : 76.1753 Min. :-45.77966
1st Qu.:-6.030431 1st Qu.:110.7305 1st Qu.: -8.06787
Median : 0.124085 Median :145.6334 Median : 0.38290
Mean :-0.027438 Mean :136.4501 Mean : -0.02724
3rd Qu.: 4.594695 3rd Qu.:159.4395 3rd Qu.: 7.30392
Max. : 5.543076 Max. :179.0380 Max. : 43.56891
IQR: STL.seasonal STL.trend STL.remainder data 10.63 48.71 15.37 45.92 % 23.1 106.1 33.5 100.0
Weights: all == 1