First we discussed the topic data structures. Regular time series data can be represented using the ts class which is already included in the basic R installation (as part of the stats package). By contrast, irregular time series data can be represented by the popular zoo objects (or xts). Most (ARIMA-)forecasting methods require regular time series.

The following R packages have been identified to support time series forecasting:

  • forecast: Excellent package by Rob Hyndman supporting all kinds of ARIMA models; even includes an automatic forecasting function (auto.arima).
  • tseries: Package supporting basic ARIMA model but also includes GARCH for volatility forecasting.
  • x12: Interface package to the X12-ARIMA program for sesonal adjustment
  • seasonal: Interface package to the X-13-ARIMA-SEATS program for sesonal adjustment.


The following potential projects covering simple time series forecasting examples have been discussed/requested:

  • Google Trends data (e.g. Influenza)
  • Company data, e.g.┬áRevenues
  • Bike Traffic in Vienna
  • Stock Prices, Volatility (GARCH)
  • Birdlife data, see data for Bavaria e.g. 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009

The projects will be presented during the November R-Meetup (contributions welcome!).


The next meetup will cover the sparklyr package presented by Roland Boubela.