ts {stats} R Documentation

## Time-Series Objects

### Description

The function ts is used to create time-series objects.

as.ts and is.ts coerce an object to a time-series and test whether an object is a time series.

### Usage

ts(data = NA, start = 1, end = numeric(), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"), class = , names = )
as.ts(x, ...)
is.ts(x)


### Arguments

 data a vector or matrix of the observed time-series values. A data frame will be coerced to a numeric matrix via data.matrix. (See also ‘Details’.) start the time of the first observation. Either a single number or a vector of two numbers (the second of which is an integer), which specify a natural time unit and a (1-based) number of samples into the time unit. See the examples for the use of the second form. end the time of the last observation, specified in the same way as start. frequency the number of observations per unit of time. deltat the fraction of the sampling period between successive observations; e.g., 1/12 for monthly data. Only one of frequency or deltat should be provided. ts.eps time series comparison tolerance. Frequencies are considered equal if their absolute difference is less than ts.eps. class class to be given to the result, or none if NULL or "none". The default is "ts" for a single series, c("mts", "ts", "matrix") for multiple series. names a character vector of names for the series in a multiple series: defaults to the colnames of data, or Series 1, Series 2, .... x an arbitrary R object. ... arguments passed to methods (unused for the default method).

### Details

The function ts is used to create time-series objects. These are vectors or matrices with class of "ts" (and additional attributes) which represent data which has been sampled at equispaced points in time. In the matrix case, each column of the matrix data is assumed to contain a single (univariate) time series. Time series must have at least one observation, and although they need not be numeric there is very limited support for non-numeric series.

Class "ts" has a number of methods. In particular arithmetic will attempt to align time axes, and subsetting to extract subsets of series can be used (e.g., EuStockMarkets[, "DAX"]). However, subsetting the first (or only) dimension will return a matrix or vector, as will matrix subsetting. Subassignment can be used to replace values but not to extend a series (see window). There is a method for t that transposes the series as a matrix (a one-column matrix if a vector) and hence returns a result that does not inherit from class "ts".

Argument frequency indicates the sampling frequency of the time series, with the default value 1 indicating one sample in each unit time interval. For example, one could use a value of 7 for frequency when the data are sampled daily, and the natural time period is a week, or 12 when the data are sampled monthly and the natural time period is a year. Values of 4 and 12 are assumed in (e.g.) print methods to imply a quarterly and monthly series respectively. As from R 4.0.0, frequency need not be a whole number. For example, frequency = 0.2 would imply sampling once every five time units.

as.ts is generic. Its default method will use the tsp attribute of the object if it has one to set the start and end times and frequency.

is.ts tests if an object is a time series. It is generic: you can write methods to handle specific classes of objects, see InternalMethods.

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

tsp, frequency, start, end, time, window; print.ts, the print method for time series objects; plot.ts, the plot method for time series objects.

For other definitions of ‘time series’ (e.g., time-ordered observations) see the CRAN task view at https://CRAN.R-project.org/view=TimeSeries.

### Examples

require(graphics)

ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959
print( ts(1:10, frequency = 7, start = c(12, 2)), calendar = TRUE)
# print.ts(.)
## Using July 1954 as start date:
gnp <- ts(cumsum(1 + round(rnorm(100), 2)),
start = c(1954, 7), frequency = 12)
plot(gnp) # using 'plot.ts' for time-series plot

## Multivariate
z <- ts(matrix(rnorm(300), 100, 3), start = c(1961, 1), frequency = 12)
class(z)