Time Series Analysis II: zoo

Data Science, Time Series

zoo: S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations)

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo’s key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics

Base Time-Series Objects stats::ts

Credit: Time Series Analysis in R Part 1: The Time Series Object by DataSciencePlus

From the base ts objects to a whole host of other packages like xts, zoo, TTR, forecast, quantmod and tidyquant, R has a large infrastructure supporting time series analysis.

Monthly/Quaterly TS

ts1 <- ts(1:10, frequency = 12, start = c(1959, 2)) # Feburary of 1959
ts1; class(ts1)
##      Feb Mar Apr May Jun Jul Aug Sep Oct Nov
## 1959   1   2   3   4   5   6   7   8   9  10
## [1] "ts"
ts2 <- ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959
ts2
##      Qtr1 Qtr2 Qtr3 Qtr4
## 1959         1    2    3
## 1960    4    5    6    7
## 1961    8    9   10

Period TS

ts3 <- ts(1:10, frequency = 7, start = c(2012, 2))
print(ts3, calendar = TRUE)
##      p1 p2 p3 p4 p5 p6 p7
## 2012     1  2  3  4  5  6
## 2013  7  8  9 10

Plot TS

gnp <- ts(cumsum(1 + round(rnorm(100), 2)),
          start = c(1954, 7), frequency = 12)
plot(gnp) # using 'plot.ts' for time-series plot

zoo class

Read vector with a time index

library(zoo)
f1 <- system.file('doc/demo1.txt', package = 'zoo')
system(paste0("head ", f1))
inrusd <- read.zoo(f1, sep = "|", format="%d %b %Y")
class(inrusd)
## [1] "zoo"
str(inrusd)
## 'zoo' series from 2005-02-10 to 2005-03-10
##   Data: num [1:20] 43.8 43.8 43.7 43.8 43.8 ...
##   Index:  Date[1:20], format: "2005-02-10" "2005-02-11" "2005-02-14" "2005-02-15" "2005-02-16" ...
head(inrusd)
## 2005-02-10 2005-02-11 2005-02-14 2005-02-15 2005-02-16 2005-02-17 
##      43.78      43.79      43.72      43.76      43.82      43.74

Read matrix with a time index

f2 <- system.file('doc/demo2.txt', package = 'zoo')
tmp <- read.table(f2, sep = ",")
z <- zoo(tmp[, 3:4], as.Date(as.character(tmp[, 2]), format="%d %b %Y"))
colnames(z) <- c("Nifty", "Junior")

head(z)
##              Nifty  Junior
## 2005-02-10 2063.35 4379.20
## 2005-02-11 2082.05 4382.90
## 2005-02-14 2098.25 4391.15
## 2005-02-15 2089.95 4367.25
## 2005-02-17 2061.90 4320.15
## 2005-02-18 2055.55 4318.15

Convert back to matrix

plain1 <- coredata(z)
head(plain1)
##        Nifty  Junior
## [1,] 2063.35 4379.20
## [2,] 2082.05 4382.90
## [3,] 2098.25 4391.15
## [4,] 2089.95 4367.25
## [5,] 2061.90 4320.15
## [6,] 2055.55 4318.15
# with rownames
plain2 <- as.matrix(z)
head(plain2)
##              Nifty  Junior
## 2005-02-10 2063.35 4379.20
## 2005-02-11 2082.05 4382.90
## 2005-02-14 2098.25 4391.15
## 2005-02-15 2089.95 4367.25
## 2005-02-17 2061.90 4320.15
## 2005-02-18 2055.55 4318.15

Selecting (subsetting)

window(z, start = as.Date("2005-02-15"), end = as.Date("2005-02-28"))
##              Nifty  Junior
## 2005-02-15 2089.95 4367.25
## 2005-02-17 2061.90 4320.15
## 2005-02-18 2055.55 4318.15
## 2005-02-21 2043.20 4262.25
## 2005-02-22 2058.40 4326.10
## 2005-02-23 2057.10 4346.00
## 2005-02-24 2055.30 4337.00
## 2005-02-25 2060.90 4305.75
## 2005-02-28 2103.25 4388.20
z[as.Date("2005-03-10")]
##             Nifty  Junior
## 2005-03-10 2167.4 4648.05

Missing Value

# only rows with data from both x and y are included in the output
m1 <- merge(inrusd, z, all = FALSE)
plot(m1)
# then extra rows will be added to the output with missing values
m2 <- merge(inrusd, z)
plot(m2)

m_approx <- na.approx(m2)# Replaced by linear interpolation via approx 
m_locf   <- na.locf(m2)  # Last Observation Carried Forward

Plot

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.1
x.Date  <- as.Date(paste(2003, 02, c(1, 3, 7, 9, 14), sep = "-"))
x       <- zoo(rnorm(5), x.Date)
xlow    <- x - runif(5)
xhigh   <- x + runif(5)
#z       <- cbind(x, xlow, xhigh)

## univariate plotting
## calling ggplot2.zoo
## autoplot(x)

## broom zoo to data.frame
ggplot(aes(x = index, y = value), data = broom::tidy(x)) +
  geom_line() + xlab("Index") + ylab("x")

R

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