DATA624_HW3
Niteen Kumar
February 23, 2019
Do exercises 6.2 and 6.3 in the Hyndman book. Please submit your Rpubs link as well as your .rmd file with your code.
library(fpp2)
library(seasonal)
library(ggplot2)
library(knitr)
library(kableExtra)- The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years.
Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend-cycle?
Monthly sales of product A for a plastics manufacturer.
#help("plastics")
summary(plastics)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 697.0 947.8 1148.0 1162.4 1362.5 1637.0plastics## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1 742 697 776 898 1030 1107 1165 1216 1208 1131 971 783
## 2 741 700 774 932 1099 1223 1290 1349 1341 1296 1066 901
## 3 896 793 885 1055 1204 1326 1303 1436 1473 1453 1170 1023
## 4 951 861 938 1109 1274 1422 1486 1555 1604 1600 1403 1209
## 5 1030 1032 1126 1285 1468 1637 1611 1608 1528 1420 1119 1013autoplot(plastics)
ggseasonplot(plastics)
ggsubseriesplot(plastics)
gglagplot(plastics)
Acf(plastics)
There is definitely seasonal fluctuations in plastics time series data. There are 5 data points availabe and frequency is 12 (the number of months).
STL is a versatile and robust method for decomposing time series. The below STL decomposition also indicates seasonality components fluctuates but with upward trend.
plastics %>%
stl(t.window=12, s.window="periodic", robust=TRUE) %>%
autoplot()
Use a classical multiplicative decomposition to calculate the trend-cycle and seasonal indices.
multiplicative_decomp <- decompose(plastics, type = "multiplicative")
multiplicative_decomp## $x
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1 742 697 776 898 1030 1107 1165 1216 1208 1131 971 783
## 2 741 700 774 932 1099 1223 1290 1349 1341 1296 1066 901
## 3 896 793 885 1055 1204 1326 1303 1436 1473 1453 1170 1023
## 4 951 861 938 1109 1274 1422 1486 1555 1604 1600 1403 1209
## 5 1030 1032 1126 1285 1468 1637 1611 1608 1528 1420 1119 1013
##
## $seasonal
## Jan Feb Mar Apr May Jun Jul
## 1 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## 2 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## 3 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## 4 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## 5 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## Aug Sep Oct Nov Dec
## 1 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
## 2 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
## 3 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
## 4 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
## 5 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
##
## $trend
## Jan Feb Mar Apr May Jun Jul
## 1 NA NA NA NA NA NA 976.9583
## 2 1000.4583 1011.2083 1022.2917 1034.7083 1045.5417 1054.4167 1065.7917
## 3 1117.3750 1121.5417 1130.6667 1142.7083 1153.5833 1163.0000 1170.3750
## 4 1208.7083 1221.2917 1231.7083 1243.2917 1259.1250 1276.5833 1287.6250
## 5 1374.7917 1382.2083 1381.2500 1370.5833 1351.2500 1331.2500 NA
## Aug Sep Oct Nov Dec
## 1 977.0417 977.0833 978.4167 982.7083 990.4167
## 2 1076.1250 1084.6250 1094.3750 1103.8750 1112.5417
## 3 1175.5000 1180.5417 1185.0000 1190.1667 1197.0833
## 4 1298.0417 1313.0000 1328.1667 1343.5833 1360.6250
## 5 NA NA NA NA NA
##
## $random
## Jan Feb Mar Apr May Jun Jul
## 1 NA NA NA NA NA NA 1.0247887
## 2 0.9656005 0.9745267 0.9750081 0.9894824 1.0061175 1.0024895 1.0401641
## 3 1.0454117 0.9953920 1.0079773 1.0142083 0.9990100 0.9854384 0.9567618
## 4 1.0257400 0.9924762 0.9807020 0.9798704 0.9684851 0.9627557 0.9917766
## 5 0.9767392 1.0510964 1.0498039 1.0299302 1.0398787 1.0628077 NA
## Aug Sep Oct Nov Dec
## 1 1.0157335 1.0040354 0.9724119 0.9961368 0.9489762
## 2 1.0230774 1.0040674 0.9962088 0.9735577 0.9721203
## 3 0.9969907 1.0132932 1.0314752 0.9910657 1.0258002
## 4 0.9776897 0.9920952 1.0133954 1.0527311 1.0665946
## 5 NA NA NA NA NA
##
## $figure
## [1] 0.7670466 0.7103357 0.7765294 0.9103112 1.0447386 1.1570026 1.1636317
## [8] 1.2252952 1.2313635 1.1887444 0.9919176 0.8330834
##
## $type
## [1] "multiplicative"
##
## attr(,"class")
## [1] "decomposed.ts"autoplot(multiplicative_decomp)
Do the results support the graphical interpretation from part a?
Yes, the multiplicative graphincal result aligns with Part A findngs of the question.
Compute and plot the seasonally adjusted data.
autoplot(plastics, series="Data") +
autolayer(trendcycle(multiplicative_decomp), series="Trend") +
autolayer(seasadj(multiplicative_decomp), series="Seasonally Adjusted") +
xlab("Year") + ylab("Monthly Sales") +
ggtitle("Plastic Sales") +
scale_colour_manual(values=c("green","blue","red"), breaks=c("Data","Seasonally Adjusted","Trend"))
Change one observation to be an outlier (e.g., add 500 to one observation), and recompute the seasonally adjusted data. What is the effect of the outlier?
change.plastics.2 <- plastics
change.plastics.2[2] <- plastics[2]+500
change.plastics.2## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1 742 1197 776 898 1030 1107 1165 1216 1208 1131 971 783
## 2 741 700 774 932 1099 1223 1290 1349 1341 1296 1066 901
## 3 896 793 885 1055 1204 1326 1303 1436 1473 1453 1170 1023
## 4 951 861 938 1109 1274 1422 1486 1555 1604 1600 1403 1209
## 5 1030 1032 1126 1285 1468 1637 1611 1608 1528 1420 1119 1013change_multiplicative_decomp <- decompose(change.plastics.2, type = "multiplicative")
autoplot(change.plastics.2, series="Data") +
autolayer(trendcycle(change_multiplicative_decomp), series="Trend") +
autolayer(seasadj(change_multiplicative_decomp), series="Seasonally Adjusted") +
xlab("Year") + ylab("Monthly Sales") +
ggtitle("Plastic Sales") +
scale_colour_manual(values=c("green","blue","red"), breaks=c("Data","Seasonally Adjusted","Trend"))
The inclusion of outlier changes the Seasonally Adjusted.
Does it make any difference if the outlier is near the end rather than in the middle of the time series?
change.plastics.middle <- plastics
change.plastics.middle[25] <- plastics[25]+500change.plastics.end <- plastics
change.plastics.end[59] <- plastics[59]+500change_multiplicative_decomp_middle <- decompose(change.plastics.middle, type = "multiplicative")
autoplot(change.plastics.middle, series="Data") +
autolayer(trendcycle(change_multiplicative_decomp_middle), series="Trend") +
autolayer(seasadj(change_multiplicative_decomp_middle), series="Seasonally Adjusted") +
xlab("Year") + ylab("Monthly Sales") +
ggtitle("Plastic Sales - Outlier in the Middle") +
scale_colour_manual(values=c("green","blue","red"), breaks=c("Data","Seasonally Adjusted","Trend"))
change_multiplicative_decomp_end <- decompose(change.plastics.end, type = "multiplicative")
autoplot(change.plastics.end, series="Data") +
autolayer(trendcycle(change_multiplicative_decomp_end), series="Trend") +
autolayer(seasadj(change_multiplicative_decomp_end), series="Seasonally Adjusted") +
xlab("Year") + ylab("Monthly Sales") +
ggtitle("Plastic Sales - outlier at the end") +
scale_colour_manual(values=c("green","blue","red"), breaks=c("Data","Seasonally Adjusted","Trend"))
We can see the seasonality adjusted and also trend changes for both outliers in the middle and at the end.
- Recall your retail time series data (from Exercise 3 in Section 2.10). Decompose the series using X11. Does it reveal any outliers, or unusual features that you had not noticed previously?
retaildata <- readxl::read_excel("C:\\NITEEN\\GitHub\\CUNY_DATA_624\\Dataset\\retail.xlsx", skip=1)
#kable(head(retaildata))myts <- ts(retaildata$A3349661X, start = c(1982,4),frequency = 12)
autoplot(myts)
Using X11 to decompose the series,
x11_retailSeries <- seas(myts, x11 = "")
autoplot(x11_retailSeries) +
ggtitle('x11 Decomposition Technique - RetailSeries')
We can see some unexpected outliers when decomposing the series using X11. There is significant change in reamainder for the period 2000-2001. Also,with the increase in trend the seasonality effect goes down