R: Time-Series Objects

ts {base} | 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(0), 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.
`start` | the time of the first observation. Either a single number or a vector of two integers, 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")` 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.

### Details

The function `ts` is used to create time-series objects. These are vector 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.

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.

The value of argument `frequency` is used when the series is sampled an integral number of times 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.ts` will use the `tsp` attribute of the object if it has one to set the start and end times and frequency.

### See Also

`tsp`, `frequency`, `start`, `end`, `time`, `window`; `print.ts`, the print method for time series objects; `plot.ts`, the plot method for time series objects. Standard package ts for many additional time-series functions.

### Examples

    ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959
    print( ts(1:10, freq = 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)
    plot(z)
    plot(z, plot.type="single", lty=1:3)

    # Ensure working arithmetic for `ts' objects :
    stopifnot(z == z)
    stopifnot(z-z == 0)

    ## A phase plot:
    data(nhtemp)
    plot(nhtemp, c(nhtemp[-1], NA), cex = .8, col="blue",
         main = "Lag plot of New Haven temperatures")
    ## a clearer way to do this would be
    library(ts)
    plot(nhtemp, lag(nhtemp, 1), cex = .8, col="blue",
         main = "Lag plot of New Haven temperatures")

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