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. Revenues
- 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.