runit.TimeSeriesData.R
In timeSeries: Financial Time Series Objects (Rmetrics)

# Rmetrics is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# Rmetrics is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA  02111-1307  USA


################################################################################


test.diffTimeSeries =
function()
{
    # diff.timeSeries - Differences a 'timeSeries' object

    # Univariate Series:
    # Multivariate Data Set:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)
    uTS
    uTS@recordIDs

    # Differencing over 1 lag
    X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = NA)
    X
    X@recordIDs
    # X = diff(x = uTS, lag = 1, diff = 1, trim = TRUE, pad = NA)
    # X
    # X@recordIDs
    X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = 0)
    X
    X@recordIDs

    # Differencing over 2 lags
    X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = NA)
    X
    X@recordIDs
    # X = diff(x = uTS, lag = 2, diff = 1, trim = TRUE, pad = NA)
    # X
    # X@recordIDs
    X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = 0)
    X
    X@recordIDs

    # Differencing twice:
    # X = diff(x = uTS, lag = 1, diff = 2, trim = FALSE, pad = NA) #ERROR
    # X
    # X@recordIDs
    # X = diff(x = uTS, lag = 2, diff = 2, trim = FALSE, pad = NA)  # ERROR
    # X
    # X@recordIDs
    # X = diff(x = uTS, lag = 1, diff = 2, trim = TRUE, pad = NA)
    # X
    # X@recordIDs
    # X = diff(x = uTS, lag = 2, diff = 2, trim = TRUE, pad = NA)
    # X
    # X@recordIDs

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.lagTimeSeries =
function()
{
    # lag.timeSeries - Lags a 'timeSeries' object

    # Univariate Series:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)

    # Multivariate Data Set:
    set.seed(4711)
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))
    mTS

    # Time Series Lags:
    X = lag(x = uTS, k = 1, trim = FALSE, units = NULL)
    X
    X@recordIDs
    X = lag(x = uTS, k = c(2,4), trim = FALSE, units = NULL)
    X
    X@recordIDs
    X = lag(x = uTS, k = c(2,4), trim = TRUE, units = NULL)
    X
    X@recordIDs
    X = lag(x = uTS, k = -1:1, trim = FALSE, units = LETTERS[1:3])
    X
    X@recordIDs

    # Multivariaye Series:
    diff(mTS, 1, 1)
    lag(mTS, 1)

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.mergeTimeSeries =
function()
{
    # merge.timeSeries - Merges two 'timeSeries' objects
    # scale.timeSeries - Centers and/or scales a 'timeSeries' object
    # summary.timeSeries - Summarizes a 'timeDate' object
    # var.timeSeries - Returns variance for a 'timeSeries' object

    # Univariate Series:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)

    # Merge:
    X = uTS
    Y = log(abs(uTS))
    merge(x = X, y = Y, units = "One column")
    colnames(Y) <- "log"
    merge(x = X, y = Y, units = c("RN", "logAbsRN"))
    merge(x = X[-6,], y = Y[-3,], units = c("RN", "logAbsRN"))
    merge(x = X[2:5,], y = Y[4:6,], units = c("RN", "logAbsRN"))

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.scaleTimeSeries =
function()
{
    # scale.timeSeries - Centers and/or scales a 'timeSeries' object

    # Univariate Series:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)

    # Multivariate Data Set:
    set.seed(4711)
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))

    # Scale:
    scale(uTS)
    scale(mTS)

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.summaryTimeSeries =
function()
{
    # summary.timeSeries - Summarizes a 'timeDate' object

    # Univariate Series:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)

    # Multivariate Data Set:
    set.seed(4711)
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))

    # Summary:
    summary(uTS)
    summary(mTS)

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.varTimeSeries =
function()
{
    # var.timeSeries - Returns variance for a 'timeSeries' object

    # Univariate Series:
    set.seed(4711)
    data = cbind(RNORM = round(rnorm(6), 2))
    charvec = timeCalendar()[1:6]
    recordIDs = data.frame(IDs = LETTERS[1:6])
    uTS = timeSeries(data, charvec, recordIDs = recordIDs)

    # Multivariate Data Set:
    set.seed(4711)
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))

    # Covariance Matrix:
    var(x = uTS, y = NULL, na.rm = FALSE)
    var(x = mTS, y = NULL, na.rm = FALSE)

    # Note, using function cov() fails, since cov() requires an atomic
    #   object as input.

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.mathOpsTimeSeries =
function()
{
    # Ops.timeSeries - Arith method for a 'timeSeries' object
    # abs.timeSeries - Returns abolute values of a 'timeSeries' object
    # sqrt.timeSeries - Returns sqrt values of a 'timeSeries' object
    # exp.timeSeries - Returns exponentials of a 'timeSeries' object
    # log.timeSeries - Returns logarithms of a 'timeSeries' object
    # quantile.timeSeries - produces sample quantiles of a 'timeSeries' object

    # Univariate Series:
    setRmetricsOptions(myFinCenter = "GMT")
    data = matrix(round(rnorm(12), 2))
    charvec = format(timeCalendar(2006))
    uTS = timeSeries(data, charvec, units = "RNORM")
    uTS

    # Multivariate Series:
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))
    mTS

    # Univariate Ops:
    uTS < 0
    uTS == abs(uTS)

    # Math Operations:
    uTS + 5
    uTS - 5
    100 * uTS
    uTS / 100
    uTS^2

    # mathematical Functions:
    log(abs(uTS))
    sqrt(exp(uTS))

    # Quantiles:
    quantile(uTS)
    quantile(uTS, probs = c(0.9, 0.95))
    quantile(uTS, probs = c(0.9, 0.95), type = 5)

    # Logical Operations:
    mTS < 0

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.subsetTimeSeries =
function()
{
    # [.timeSeries - subsets of a 'timeSeries' object
    # cut.timeSeries - cuts a block from a 'timeSeries' object
    # head.timeSeries - returns the head of a 'timeSeries' object
    # tail.timeSeries - returns the tail of a 'timeSeries' object
    # outlier.timeSeries - Removes outliers from a 'timeSeries' object

    # Univariate Series:
    setRmetricsOptions(myFinCenter = "GMT")
    data = matrix(round(rnorm(12), 2))
    charvec = format(timeCalendar(2006))
    uTS = timeSeries(data, charvec, units = "RNORM")
    uTS

    # Multivariate Series:
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))
    mTS

    # Subsets:
    X = uTS[4:6, ]
    X
    X@recordIDs

    # Head and Tail:
    head(uTS)
    tail(uTS)
    head(mTS)
    tail(mTS)

    # Data Subsetting:
    mTS[, 1]            # First Series
    mTS[4:6, 1]         # Second Quarter

    # Return Value:
    return()
}


# ------------------------------------------------------------------------------


test.dimOpsTimeSeries =
function()
{
    # dim - Returns the dimension of a 'timeSeries' object
    # dimnames - Returns the dimension names of a 'timeSeries' object
    # colnames<-.timeS* - Assigns column names to a 'timeSeries' object
    # rownames<-.timeS* - Assigns row names to a 'timeSeries' object
    # is.array.timeSeries - Allows that NCOL and NROW work properly

    # Univariate Series:
    setRmetricsOptions(myFinCenter = "GMT")
    data = matrix(round(rnorm(12), 2))
    charvec = format(timeCalendar(2006))
    uTS = timeSeries(data, charvec, units = "RNORM")
    uTS

    # Multivariate Series:
    data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) )
    charvec = format(timeCalendar(2006))
    mTS = timeSeries(data, charvec, units = c("RNORM", "RT"))
    mTS

    # Dimension:
    dim(uTS) == c(12, 1)
    dimnames(uTS)

    # Column and Rownames:
    # X = uTS
    # colnames(X) = "X"
    # rownames(X) = as.character(timeCalendar()+24*3600)
    # X
    # series(X)

    # Array:
    is.array(uTS)

    # Number of Columns/Rows:
    NCOL(uTS)
    NROW(uTS)
    ncol(uTS)
    nrow(uTS)

    # Return Value:
    return()
}


################################################################################