hts (version 6.0.3)

gts: Create a grouped time series

Description

Method for creating grouped time series.

Usage

gts(y, groups, gnames = rownames(groups), characters)
is.gts(xts)
# S3 method for gts
print(x, ...)
# S3 method for gts
summary(object, ...)

Value

bts

Multivariate time series contains the bottom level series

groups

Information about the groups of a grouped time series

labels

Information about the labels that are used for plotting.

Arguments

y

A matrix or multivariate time series contains the bottom level series.

groups

Group matrix indicates the group structure, with one column for each series when completely disaggregated, and one row for each grouping of the time series. It allows either a numerical matrix or a matrix consisting of strings that can be used for labelling. If the argument characters is used, then groups will be automatically generated within the function.

gnames

Specify the group names.

characters

A vector of integers, or a list containing vectors of integers, indicating the segments in which bottom level names can be read in order to construct the corresponding grouping matrix and its labels. A list class is used when a grouped time series includes one or more hierarchies. For example, a grouped time series may involve a geographical grouping and a product grouping, with each of them associated with a 2-level hierarchy. In this situation, a bottom level name such as "VICMelbAB" would indicate the state "VIC" (3 characters) followed by the city "Melb" (4 characters), then the product category "A" (1 character) followed by the sub-product category "B" (1 character). In this example, the specification of characters is list(c(3, 4), c(1, 1)), where the first element c(3, 4) corresponds to the geographical hierarchy and the second element corresponds to the product hierarchy. In the special case where there is a non-hierarchical grouped time series, a vector of integers is also possible. For example, a grouped time series may involve state, age and sex grouping variables. In this situation, a bottom level name such as "VIC1F" would indicate the state "VIC", age group "1" and sex "F". Because none of these is hierarchical, we could specify characters = list(3, 1, 1), or as a simple numeric vector: characters = c(3, 1, 1). This implies its non-hierarchical structure and its characters segments. Again, all bottom level names must be of the same length. Currently, the use of characters only supports 2-way cross-products for grouping variables. Specifying groups is more general (but more complicated), as any combination of grouping variables can be used.

xts

gts object.

x

gts object.

...

Extra arguments passed to print and summary.

object

gts object.

Author

Earo Wang and Rob J Hyndman

References

Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579--2589. https://robjhyndman.com/publications/hierarchical/

See Also

hts, accuracy.gts, forecast.gts, plot.gts

Examples

# Example 1 illustrating the usage of the "groups" argument
abc <- ts(5 + matrix(sort(rnorm(1600)), ncol = 16, nrow = 100))
sex <- rep(c("female", "male"), each = 8)
state <- rep(c("NSW", "VIC", "QLD", "SA", "WA", "NT", "ACT", "TAS"), 2)
gc <- rbind(sex, state)  # a matrix consists of strings.
gn <- rbind(rep(1:2, each = 8), rep(1:8, 2))  # a numerical matrix
rownames(gc) <- rownames(gn) <- c("Sex", "State")
x <- gts(abc, groups = gc)
y <- gts(abc, groups = gn)

# Example 2 with two simple hierarchies (geography and product) to show the argument "characters"
bnames1 <- c("VICMelbAA", "VICMelbAB", "VICGeelAA", "VICGeelAB",
             "VICMelbBA", "VICMelbBB", "VICGeelBA", "VICGeelBB",
             "NSWSyndAA", "NSWSyndAB", "NSWWollAA", "NSWWollAB",
             "NSWSyndBA", "NSWSyndBB", "NSWWollBA", "NSWWollBB")
bts1 <- matrix(ts(rnorm(160)), ncol = 16)
colnames(bts1) <- bnames1
x1 <- gts(bts1, characters = list(c(3, 4), c(1, 1)))

# Example 3 with a non-hierarchical grouped time series of 3 grouping variables (state, age and sex)
bnames2 <- c("VIC1F", "VIC1M", "VIC2F", "VIC2M", "VIC3F", "VIC3M",
             "NSW1F", "NSW1M", "NSW2F", "NSW2M", "NSW3F", "NSW3M")
bts2 <- matrix(ts(rnorm(120)), ncol = 12)
colnames(bts2) <- bnames2
x2 <- gts(bts2, characters = c(3, 1, 1))