TimeSeriesClass {timeSeries}R Documentation

timeSeries Class

Description

A collection and description of functions and methods dealing with regular and irregular 'timeSeries' objects. Dates and times are implemented as 'timeDate' objects.

Functions to generate and modify 'timeSeries' objects:

timeSeries Creates a 'timeSeries' object from scratch,
readSeries Reads a 'timeSeries' from a spreadsheet file.

Data Slot and classification of 'timeSeries' objects:

seriesData Extracts data slot from a 'timeSeries'.

Usage

timeSeries(data, charvec, units = NULL, format = NULL, zone = "", 
    FinCenter = "", recordIDs = data.frame(), title = NULL, 
    documentation = NULL, ...)
       
readSeries(file, header = TRUE, sep = ";", zone = "", 
    FinCenter = "",  ...)

seriesData(object)

Arguments

charvec a character vector of dates and times or any objects which can be coerced to a timeDate object.
data a matrix object or any objects which can be coereced to a matrix.
documentation optional documentation string, or a vector of character strings.
file the filename of a spreadsheet data set from which to import the data records.
FinCenter a character with the the location of the financial center named as "continent/city".
header a logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: 'header' is set to 'TRUE' if and only if the first row contains one fewer field than the number of columns.
format the format specification of the input character vector,
[as.timeSeries] -
a character string with the format in POSIX notation to be passed to the time series object.
object [is][seriesData][seriesPositions][show][summary] - an object of class timeSeries.
recordIDs a data frame which can be used for record identification information.
[print] -
a logical value. Should the recordIDs printed together with the data matrix and time series positions?
sep [readSeries] -
the field seperator used in the spreadsheet file to separate columns.
title an optional title string, if not specified the inputs data name is deparsed.
units an optional character string, which allows to overwrite the current column names of a timeSeries object. By default NULL which means that the column names are selected automatically.
zone the time zone or financial center where the data were recorded.
... arguments passed to other methods.

Details

Generation of Time Series Objects:

We have defined a timeSeries class which is in many aspects similar to the S-Plus class with the same name, but has also some important differences. The class has seven Slots, the 'Data' slot which holds the time series data in matrix form, the 'position' slot which holds the time/date as a character vector, the 'format' and 'FinCenter' slots which are the same as for the 'timeDate' object, the 'units' slot which holds the column names of the data matrix, and a 'title' and a 'documentation' slot which hold descriptive character strings. Date and time is managed in the same way as for timeDate objects.

Value

timeSeries
readSeries
returnSeries

return a S4 object of class timeSeries.

orderStatistics
returns ...

series

extracts the @.Data slot from a timeSeries object and is equivalent to as.amtrix.

Note

These functions were written for Rmetrics users using R and Rmetrics under Microsoft's Windows operating system where timze zones, daylight saving times and holiday calendars are insuffeciently supported.

Examples

## Load Microsoft Data -
   # Microsoft Data: 
   setRmetricsOptions(myFinCenter = "GMT")
   data(MSFT)
   head(MSFT)

## Create a timeSeries Object, The Direct Way ...
   Close = MSFT[, 5]
   head(Close)
   
## Create a timeSeries Object from Scratch - 
   data = as.matrix(MSFT[, 4])
   charvec = rownames(MSFT)
   Close = timeSeries(data, charvec, units = "Close")
   head(Close)
   c(start(Close), end(Close))
    
## Cut out April Data from 2001 - 
   tsApril01 = window(Close, "2001-04-01", "2001-04-30") 
   tsApril01

## Compute Continuous Returns - 
   returns(tsApril01)
   
## Compute Discrete Returns - 
   returns(tsApril01, type = "discrete")
   
## Compute Discrete Returns, Don't trim -
   returns(tsApril01, trim = FALSE)
   
## Compute Discrete Returns, Use Percentage Values - 
   tsRet = returns(tsApril01, percentage = TRUE, trim = FALSE)
   tsRet
     
## Aggregate Weekly - 
   GoodFriday(2001)
   to = timeSequence(from = "2001-04-11", length.out = 3, by = "week") 
   from = to - 6*24*3600
   from
   to
   applySeries(tsRet, from, to, FUN = sum)

## Create large timeSeries objects with different 'charvec' object classes - 
   # charvec is a 'timeDate' object
   head(timeSeries(1:1e6L, timeSequence(length.out = 1e6L, by = "sec")))
   head(timeSeries(1:1e6L, seq(Sys.timeDate(), length.out = 1e6L, by = "sec")))
   # 'charvec' is a 'POSIXt' object
   head(timeSeries(1:1e6L, seq(Sys.time(), length.out = 1e6L, by = "sec")))
   # 'charvec' is a 'numeric' object
   head(timeSeries(1:1e6L, 1:1e6L))
[Package timeSeries version 2110.87 Index]