R: time series
Sumber: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/ts.html
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 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", "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".
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 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. 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.
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) head(z) # as "matrix" plot(z) plot(z, plot.type = "single", lty = 1:3)
## A phase plot:
plot(nhtemp, lag(nhtemp, 1), cex = .8, col = "blue",
main = "Lag plot of New Haven temperatures")
[Package stats version 3.6.0 Index]