fSeries-package: Utilities and Tools Package

Description

Package of time series tools and utilities.

Arguments

Overview of Topics:

1. S4 timeSeries Class Definition 2. Creating timeSeries Objects 3. Printing and Plotting timeSeries Objects 4. Modifying 'timeSeries' Objects

1. S4 timeSeries Class

timeSeries Class Definition 'timeSeries' ... Extracting Information from a timeSeries object: isUnivariate isMultivariate

2. Creating timeSeries Objects

'timeSeries' objects can be created in several ways. One can create them from scratch, or one can read them from a file. If we have a file it is assumed that the first column holds a character string with the date/time positions, named "charvec", and the remaining column(s), depending if we consider the univariate or multivariate case the numeric time series records named "data". Create a 'timeSeries' object from scratch: timeSeries Read a time Series from a file: readSeries Attach a 'timeSeries' object to the search path: List of Functions: ll{ timeSeries Creates a 'timeSeries' from scratuch, readSeries reads a time series from a file, attach attaches a 'timeSeries' object, detach detaches a 'timeSeries' object [see base package]. }

3. Printing and Plotting 'timeSeries' Objects

List of Functions: ll{ print ..., plot ..., points ..., lines ... . }

4. Modifying 'timeSeries' Objects

If we have a price/index series or a return series, it is often required taht we have to convert from one to the other representation. So Rmetrics makes functions available which compute returns from price/index series or the cumulated series from returns. Further modifications are concerned with drawdowns, durations, spreads and midquotes. List of Functions: ll{ returns Compute returns from prices or indexes, cumulated compute cumulated series from a returns, drawdowns compute series of drawdowns from financial returns, durations compute durations from a financial time series, spreads compute spreads from a price/index stream, miquotes compute mid quotes from a price/index stream. }

Subsetting timeSeries Objects

'timeSeries' objects can subsetted in several ways. The method window (and the deprecated function cut) extracts the subset of a 'timeSeries' observed between the times from and to. The methods head and tail return the first or last part of a time series for a specified length. The method outlier can be used to remove huge "outliers" which may appear for example through the redefinition of an index (it should not be used for outlier detection). There are other functions provided which can be considered in broader sense as subset functions. For example the function fapply(x, from, to, FUN, ...) can be used to subset a time series with very complex rules, e.g. subset Mondays from a series, subset the last Thursdays in every Month, subset from a daily series open (first), high, low, close (last) prices to a end-of-month time series, etc. As another example, we mention the function aggreagte which can data records subset to monthly or quarterly information with information specified by the function FUN aggregate(x, by = c("monthly", "quarterly"), FUN = colMeans, units = NULL, ...) List of Functions: ll{ "[" ..., "[<-" ..., window ..., head ..., tail ..., outlier ..., fapply ..., aggreagte ... . }

Merging and Binding timeSeries Objects

List of Functions: ll{ merge merges ..., rbind binds ..., lag lags ... . }

Reordering the columns of timeSeries Objects

List of Functions: ll{ orderColnames ..., sortColname ..., sampleColnames ..., statsColnames ..., pcaColnames ..., hclustColnames ... . }

Renaming column and rows of timeSeries Objects

List of Functions: ll{ dim ..., dimnames ..., colnames<- ..., rownames<- ..., is.array ... }

Mathematical Operations

List of Functions: ll{ Ops.timeSeries S3: Arith method for a 'timeSeries' object, abs Returns absolute values of a 'timeSeries' object, sqrt Returns square root of a 'timeSeries' object, exp Returns the exponential values of a 'timeSeries' object, log Returns the logarithm of a 'timeSeries' object, sign Returns the signs of a 'timeSeries' object, diff Differences a 'timeSeries' object, scale Centers and/or scales a 'timeSeries' object, quantile Returns quantiles of an univariate 'timeSeries'. }

Handling Missing Data

List of Functions: ll{ na.omit Handles NAs in a timeSeries object, removeNA removes NAs from a matrix object, substituteNA substitutes NAs by zero, the column mean or median, interpNA interpolates NAs using R's "approx" function. }

Special Daily and Monthly Operations

Note, a daily time series is not necessarily a series which has only position dates, rather it should mean that we have not more than one record per day. The availability of daily data, e.g. end of day data, is in real time usually at the same local time every working day, but this may be influenced due to holidays or due to the fact that you get the data from a financial center located in another time zone than your own. If all all these facts can be neglected and only date positions without time information is relevant and we speak losely of "daily" data records. It is also worth to note, that Rmetrics uses in this case by default the ISO-8601 format "%Y-%m-%d". In the case of monthly data, it is important to note that Rmetrics always expects the full date for the position vector and not only the years ans monhts. ... List of Functions: ll{ dummyDailySeries Creates a dummy daily 'timeSeries' object, alignDailySeries Aligns a daily 'timeSeries' to new positions, rollDailySeries Rolls daily a 'timeSeries' on a given period, ohlcDailyPlot Plots open high low close bar chart, countMonthlyRecords Returns a series with monthly counts of records, isMonthly Decides if the series consists of monthly records, rollMonthlyWindows Returns start and end dates for rolling time windows, rollMonthlySeries Rolls monthly a 'timeSeries' on a given period. }

Column and Row Statistics

List of Functions: ll{ colStats ..., rowStats ... . }

Coercion of timeSeries Objects

List of Functions:

as is.timeSeries as.timeSeries as.timeSeries.default as.timeSeries.numeric as.timeSeries.data.frame as.timeSeries.matrix as.timeSeries.ts as.timeSeries.character as.timeSeries.zoo as.vector.timeSeries as.matrix.timeSeries as.data.frame.timeSeries as.ts.timeSeries