The goal of vctsfr is to display time series and, optionally, their future values and forecasts for those future values along with prediction intervals for the forecasts. vctsfr is especially useful when you want to visually compare the forecasts of several models on collections of time series. The package contains a web-based GUI to facilitate this comparison.

You can install the development version of vctsfr from GitHub with:

```
# install.packages("devtools")
::install_github("franciscomartinezdelrio/vctsfr") devtools
```

If the package is accepted in CRAN you will be able to install it from CRAN with:

`install.packages("vctsfr")`

The best way of learning to use the package is to read its vignette.
Here, we show some functions in action. The `plot_ts()`

function is useful to display a time series and a forecast for its
future values:

```
library(vctsfr)
library(forecast)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
<- ets(USAccDeaths)
ets_fit <- forecast(ets_fit, h = 12)
ets_f plot_ts(USAccDeaths, prediction = ets_f$mean, method = "ets")
```

To compare several forecasts for a time series you can use the
`plot_predictions()`

function:

```
library(vctsfr)
library(forecast)
<- window(USAccDeaths, end = c(1977, 12)) # historical values
timeS <- window(USAccDeaths, start = c(1978, 1)) # "future" values
fut <- ets(timeS) # exponential smoothing fit
ets_fit <- forecast(ets_fit, h = length(fut)) # exponential smoothing forecast
ets_f <- auto.arima(timeS) # ARIMA fit
arima_fit <- forecast(arima_fit, h = length(fut)) # ARIMA forecast
arima_f plot_predictions(timeS, future = fut,
predictions = list(ets = ets_f$mean, arima = arima_f$mean) )
```

It is also possible to create a collection of time series (holding optionally their future values, forecasts and prediction intervals for the forecasts) and display them:

```
# A collection of two time series
<- list(ts_info(USAccDeaths), ts_info(UKDriverDeaths))
collection plot_collection(collection, number = 2)
```

However, the best way of navigating and displaying the information in a collection of time series is through the web-based GUI:

`GUI_collection(collection)`