Create objects from class 'timeSeries'

timeSeries creates a "timeSeries" object from scratch.

Usage

timeSeries(data, charvec, units = NULL, format = NULL, zone = "", 
           FinCenter = "", recordIDs = data.frame(), title = NULL, 
           documentation = NULL, ...)

Arguments

data: a matrix object or any objects which can be coerced to a matrix.
charvec: a character vector of dates and times or any objects which can be coerced to a "timeDate" object.
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.
format: the format specification of the input character vector, a character string with the format in POSIX notation.
zone: the time zone or financial center where the data were recorded.
FinCenter: a character with the the location of the financial center named as "continent/city".
recordIDs: for timeSeries, a data frame which can be used for record identification.
title: an optional title string, if not specified the input's data name is deparsed.
documentation: optional documentation string, or a vector of character strings.
...: arguments passed to other methods.

Value

an S4 object of class "timeSeries"

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.

as.timeSeries also creates "timeSeries" objects. as.timeSeries(x) is mostly equivalent to timeSeries(x) but the two functions have different methods. Beside that, the main difference between the two functions is that as.timeSeries doesn't accept additional arguments. The one argument call is naturally interpreted as ‘convert to’, so as.timeSeries is more expressive and is recommended in that case.

"timeSeries" methods are provided for many base R functions, including arithmetic operations, mathematical functions, print, summary, and time series functions. Not all are explicitly documented, since they can just be used.

Examples

## Load Microsoft data -
   # Microsoft Data: 
   setRmetricsOptions(myFinCenter = "GMT")
   data(MSFT)
   head(MSFT)
#> GMT 
#>               Open    High     Low   Close   Volume
#> 2000-09-27 63.4375 63.5625 59.8125 60.6250 53077800
#> 2000-09-28 60.8125 61.8750 60.6250 61.3125 26180200
#> 2000-09-29 61.0000 61.3125 58.6250 60.3125 37026800
#> 2000-10-02 60.5000 60.8125 58.2500 59.1250 29281200
#> 2000-10-03 59.5625 59.8125 56.5000 56.5625 42687000
#> 2000-10-04 56.3750 56.5625 54.5000 55.4375 68226700

## Create a 'timeSeries' object, the direct Way ...
   Close <- MSFT[, 5]
   head(Close)
#> GMT 
#>              Volume
#> 2000-09-27 53077800
#> 2000-09-28 26180200
#> 2000-09-29 37026800
#> 2000-10-02 29281200
#> 2000-10-03 42687000
#> 2000-10-04 68226700
   
## Create a 'timeSeries' object from scratch - 
   data <- as.matrix(MSFT[, 4])
   charvec <- rownames(MSFT)
   Close <- timeSeries(data, charvec, units = "Close")
   head(Close)
#> GMT 
#>              Close
#> 2000-09-27 60.6250
#> 2000-09-28 61.3125
#> 2000-09-29 60.3125
#> 2000-10-02 59.1250
#> 2000-10-03 56.5625
#> 2000-10-04 55.4375
   c(start(Close), end(Close))
#> GMT
#> [1] [2000-09-27] [2001-09-27]

## Cut out April data from 2001 - 
   tsApril01 <- window(Close, "2001-04-01", "2001-04-30") 
   tsApril01
#> GMT 
#>              Close
#> 2001-04-02 55.8125
#> 2001-04-03 53.3750
#> 2001-04-04 51.9375
#> 2001-04-05 56.7500
#> 2001-04-06 56.1875
#> 2001-04-09 57.1500
#> 2001-04-10 59.6800
#> 2001-04-11 60.0400
#> 2001-04-12 62.1800
#> 2001-04-16 60.7900
#> 2001-04-17 61.4800
#> 2001-04-18 65.4300
#> 2001-04-19 68.0400
#> 2001-04-20 69.0000
#> 2001-04-23 68.2500
#> 2001-04-24 67.5500
#> 2001-04-25 69.6900
#> 2001-04-26 69.1300
#> 2001-04-27 67.1200
#> 2001-04-30 67.7500

## Compute Continuous Returns - 
   returns(tsApril01)
#> GMT 
#>                   Close
#> 2001-04-03 -0.044655387
#> 2001-04-04 -0.027301399
#> 2001-04-05  0.088614584
#> 2001-04-06 -0.009961344
#> 2001-04-09  0.016985078
#> 2001-04-10  0.043317566
#> 2001-04-11  0.006014051
#> 2001-04-12  0.035022398
#> 2001-04-16 -0.022608103
#> 2001-04-17  0.011286617
#> 2001-04-18  0.062268950
#> 2001-04-19  0.039114899
#> 2001-04-20  0.014010737
#> 2001-04-23 -0.010929071
#> 2001-04-24 -0.010309370
#> 2001-04-25  0.031188771
#> 2001-04-26 -0.008068045
#> 2001-04-27 -0.029506728
#> 2001-04-30  0.009342398
   
## Compute Discrete Returns - 
   returns(tsApril01, type = "discrete")
#> GMT 
#>                   Close
#> 2001-04-03 -0.044655387
#> 2001-04-04 -0.027301399
#> 2001-04-05  0.088614584
#> 2001-04-06 -0.009961344
#> 2001-04-09  0.016985078
#> 2001-04-10  0.043317566
#> 2001-04-11  0.006014051
#> 2001-04-12  0.035022398
#> 2001-04-16 -0.022608103
#> 2001-04-17  0.011286617
#> 2001-04-18  0.062268950
#> 2001-04-19  0.039114899
#> 2001-04-20  0.014010737
#> 2001-04-23 -0.010929071
#> 2001-04-24 -0.010309370
#> 2001-04-25  0.031188771
#> 2001-04-26 -0.008068045
#> 2001-04-27 -0.029506728
#> 2001-04-30  0.009342398
   
## Compute Discrete Returns, Don't trim -
   returns(tsApril01, trim = FALSE)
#> GMT 
#>                   Close
#> 2001-04-02           NA
#> 2001-04-03 -0.044655387
#> 2001-04-04 -0.027301399
#> 2001-04-05  0.088614584
#> 2001-04-06 -0.009961344
#> 2001-04-09  0.016985078
#> 2001-04-10  0.043317566
#> 2001-04-11  0.006014051
#> 2001-04-12  0.035022398
#> 2001-04-16 -0.022608103
#> 2001-04-17  0.011286617
#> 2001-04-18  0.062268950
#> 2001-04-19  0.039114899
#> 2001-04-20  0.014010737
#> 2001-04-23 -0.010929071
#> 2001-04-24 -0.010309370
#> 2001-04-25  0.031188771
#> 2001-04-26 -0.008068045
#> 2001-04-27 -0.029506728
#> 2001-04-30  0.009342398
   
## Compute Discrete Returns, Use Percentage Values - 
   tsRet <- returns(tsApril01, percentage = TRUE, trim = FALSE)
   tsRet
#> GMT 
#>                 Close
#> 2001-04-02         NA
#> 2001-04-03 -4.4655387
#> 2001-04-04 -2.7301399
#> 2001-04-05  8.8614584
#> 2001-04-06 -0.9961344
#> 2001-04-09  1.6985078
#> 2001-04-10  4.3317566
#> 2001-04-11  0.6014051
#> 2001-04-12  3.5022398
#> 2001-04-16 -2.2608103
#> 2001-04-17  1.1286617
#> 2001-04-18  6.2268950
#> 2001-04-19  3.9114899
#> 2001-04-20  1.4010737
#> 2001-04-23 -1.0929071
#> 2001-04-24 -1.0309370
#> 2001-04-25  3.1188771
#> 2001-04-26 -0.8068045
#> 2001-04-27 -2.9506728
#> 2001-04-30  0.9342398
     
## Aggregate Weekly - 
   GoodFriday(2001)
#> GMT
#> [1] [2001-04-13]
   to <- timeSequence(from = "2001-04-11", length.out = 3, by = "week") 
   from <- to - 6*24*3600
   from
#> GMT
#> [1] [2001-04-05] [2001-04-12] [2001-04-19]
   to
#> GMT
#> [1] [2001-04-11] [2001-04-18] [2001-04-25]
   applySeries(tsRet, from, to, FUN = sum)
#> GMT 
#>                Close
#> 2001-04-11 14.496993
#> 2001-04-18  8.596986
#> 2001-04-25  6.307597

## Create large 'timeSeries' objects with different 'charvec' object classes - 
   # charvec is a 'timeDate' object
   head(timeSeries(1:1e6L, timeSequence(length.out = 1e6L, by = "sec")))
#> GMT 
#>                     TS.1
#> 2023-12-16 19:35:29    1
#> 2023-12-16 19:35:30    2
#> 2023-12-16 19:35:31    3
#> 2023-12-16 19:35:32    4
#> 2023-12-16 19:35:33    5
#> 2023-12-16 19:35:34    6
   head(timeSeries(1:1e6L, seq(Sys.timeDate(), length.out = 1e6L, by = "sec")))
#> GMT 
#>                     TS.1
#> 2024-01-14 19:35:29    1
#> 2024-01-14 19:35:30    2
#> 2024-01-14 19:35:31    3
#> 2024-01-14 19:35:32    4
#> 2024-01-14 19:35:33    5
#> 2024-01-14 19:35:34    6
   # 'charvec' is a 'POSIXt' object
   head(timeSeries(1:1e6L, seq(Sys.time(), length.out = 1e6L, by = "sec")))
#> GMT 
#>                     TS.1
#> 2024-01-14 19:35:29    1
#> 2024-01-14 19:35:30    2
#> 2024-01-14 19:35:31    3
#> 2024-01-14 19:35:32    4
#> 2024-01-14 19:35:33    5
#> 2024-01-14 19:35:34    6
   # 'charvec' is a 'numeric' object
   head(timeSeries(1:1e6L, 1:1e6L))
#> GMT 
#>                     TS.1
#> 1970-01-01 00:00:01    1
#> 1970-01-01 00:00:02    2
#> 1970-01-01 00:00:03    3
#> 1970-01-01 00:00:04    4
#> 1970-01-01 00:00:05    5
#> 1970-01-01 00:00:06    6

Usage

Arguments

Value

Details

See also

Examples