The R ecosystem knows a vast number of time series standards. Instead of creating the ultimate 15th time series class, tsbox provides a set of tools that are **agnostic towards the existing standards**. The tools also allow you to handle time series as plain data frames, thus making it easy to deal with time series in a dplyr or data.table workflow.

To install the stable version from CRAN:

`install.packages("tsbox")`

To install the development version:

```
# install.packages("remotes")
remotes::install_github("christophsax/tsbox")
```

tsbox is built around a set of converters, which convert time series stored as **ts**, **xts**, **data.frame**, **data.table**, **tibble**, **zoo**, **tsibble**, **tibbletime** or **timeSeries** to each other:

```
library(tsbox)
x.ts <- ts_c(fdeaths, mdeaths)
x.xts <- ts_xts(x.ts)
x.df <- ts_df(x.xts)
x.dt <- ts_dt(x.df)
x.tbl <- ts_tbl(x.dt)
x.zoo <- ts_zoo(x.tbl)
x.tsibble <- ts_tsibble(x.zoo)
x.tibbletime <- ts_tibbletime(x.tsibble)
x.timeSeries <- ts_timeSeries(x.tibbletime)
all.equal(ts_ts(x.timeSeries), x.ts) # TRUE
```

Because this works reliably, it is easy to write functions that work for all classes. So whether we want to **smooth**, **scale**, **differentiate**, **chain**, **forecast**, **regularize** or **seasonally adjust** a time series, we can use the same commands to whatever time series class at hand:

A set of helper functions makes it easy to combine or align multiple time series of all classes:

```
# collect time series as multiple time series
ts_c(ts_dt(EuStockMarkets), AirPassengers)
ts_c(EuStockMarkets, mdeaths)
# combine time series to a new, single time series
ts_bind(ts_dt(mdeaths), AirPassengers)
ts_bind(ts_xts(AirPassengers), ts_tbl(mdeaths))
```

Plotting all kinds of classes and frequencies is as simple as it should be. And we finally get a legend!

`ts_plot(ts_scale(ts_c(mdeaths, austres, AirPassengers, DAX = EuStockMarkets[ ,'DAX'])))`