The R ecosystem knows a ridiculous number of time series classes. So, I decided to create a new universal standard that finally covers everyone’s use case… Ok, just kidding!

tsbox, now freshly on CRAN, provides a set of tools that are agnostic towards existing time series classes. It is built around a set of converters, which convert time series stored as ts, xts, data.frame, data.table, tibble, zoo, tsibble or timeSeries to each other.

To install the stable version from CRAN:

```
install.packages("tsbox")
```

To get an idea how easy it is to switch from one class to another, consider this:

```
library(tsbox)
x.ts <- ts_c(mdeaths, fdeaths)
x.xts <- ts_xts(x.ts)
x.df <- ts_df(x.xts)
x.tbl <- ts_tbl(x.df)
x.dt <- ts_tbl(x.tbl)
x.zoo <- ts_zoo(x.dt)
x.tsibble <- ts_tsibble(x.zoo)
x.timeSeries <- ts_timeSeries(x.tsibble)
```

We jump form good old `ts`

objects to`xts`

, store our time series in various
data frames and convert them to some highly specialized time series formats.

Because these converters work nicely, we can use them to make functions class-agnostic. If a class-agnostic function works for one class, it works for all:

```
ts_scale(x.ts)
ts_scale(x.xts)
ts_scale(x.df)
ts_scale(x.dt)
ts_scale(x.tbl)
```

`ts_scale`

normalizes one or multiple series, by subtracting the mean and
dividing by the standard deviation. It works like a ‘generic’ function: You can
apply it on any time series object, and it will return an object of the same
class as its input.

So, whether we want to smooth, scale, differentiate, chain-link, forecast, regularize or seasonally adjust a series, we can use the same commands to whatever time series at hand. tsbox offers a comprehensive toolkit for the basics of time series manipulation. Here are some additional operations:

```
ts_pc(x.ts) # percentage change rates
ts_forecast(x.xts) # forecast, by exponential smoothing
ts_seas(x.df) # seasonal adjustment, by X-13
ts_frequency(x.dt, "year") # convert to annual frequency
ts_span(x.tbl, "-1 year") # limit time span to final year
```

There are many more. Because they all start with `ts_`

, you can use
auto-complete to see what’s around. Most conveniently, there is a time series
plot function that works for all classes and frequencies:

```
ts_plot(
`Airline Passengers` = AirPassengers,
`Lynx trappings` = ts_df(lynx),
`Deaths from Lung Diseases` = ts_xts(fdeaths),
title = "Airlines, trappings, and deaths",
subtitle = "Monthly passengers, annual trappings, monthly deaths"
)
```

There is also a version that uses ggplot2 and has the same syntax.

You may have wondered why we treated data frames as a time series class. The spread of dplyr and data.table has given data frames a boost and made them one of the most popular data structures in R. So, storing time series in a data frame is an obvious consequence. And even if you don’t intend to keep time series in data frames, this is still the format in which you import and export your data. tsbox makes it easy to switch from data frames to time series and back.

tsbox includes tools to
make existing functions class-agnostic. To do so, the `ts_`

function can be used
to wrap any function that works with time series. For a function that works on
`"ts"`

objects, this is as simple as that:

```
ts_rowsums <- ts_(rowSums)
ts_rowsums(ts_c(mdeaths, fdeaths))
```

Note that `ts_`

returns a function, which can be used with or without a name.

In case you are wondering, tsbox uses data.table as a backend, and makes use of its incredibly efficient reshaping facilities, its joins and rolling joins. And thanks to anytime, tsbox will be able to recongnize almost any date format without manual intervention.

So, enjoy some relieve in R’s time series class struggle.

Website: www.tsbox.help

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Phone: +41 (0)43 540 26 91

info@christophsax.com

© 2016 Christoph Sax Data Analytics