## Time-Series Objects

## Usage

    ts(data = NA, start = 1, end = numeric(0), frequency = 1,
       deltat = 1, ts.eps = .Options$ts.eps)
    as.ts(x)
    is.ts(x)

    print(ts.obj, calendar, ...)
    plot(ts.obj, ...)

## Arguments

`data` | a vector or matrix the observed time-series values.
`start` | the time of the first observation.
`end` | the time of the last observation.
`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`.

## Description

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.

The value of argument `frequency` is used when the series is sampled an interal 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.

`start` and `end` can either be integers which correspond to natural time units, or vectors of two integers, which give a natural time unit and a (1-based) number of samples into the time unit.

`as.ts` and `is.ts` respectively coerce a vector into a time-series and test whether an object is a time series.

Time series have methods associated with the generic `print` and `plot` functions. The argument `calendar` to the print method can be used to enable/disable the display of information about month names, quarter names or year when printing.

## See Also

`frequency`, `start`, `end`, `time`, `window`.

## 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)

    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)

    ## A phase plot:
    data(nhtemp)
    plot(nhtemp, c(nhtemp[-1],NA), cex = .8, col="blue",
    	main="Lag plot of New Haven temperatures")

    ## A phase plot:
    data(nhtemp)
    plot(nhtemp, c(nhtemp[-1],NA), cex = .8, col="blue",
    	main="Lag plot of New Haven temperatures")

[Package Contents]