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Subsequent to development of the automated feature detection routines, coding RBF features now takes under a minute. The only feature that continues to be manually coded is causal forces. This has provided great opportunity for us to participate in competitions such as the M-3 Competition . In this competition which required participants to forecast both short (e.g. monthly, quarterly) and long (annual) time series, we faced two challenges. Probably the most critical was that related to forecasting short period data. RBF had been developed, validated, and tested on annual data. Our rules would require recalibration to work with short period data. Furthermore, we would need to build a seasonality component. A second challenge was coding 3003 series on causal forces. Three key strategies were used:

  • We assumed that causal forces for all the 3003 time series were unknown.
  • We included a simple seasonality component in RBF.
  • We calibrated some key rules in order to forecast the short period data.

Despite these constraints, particularly that related to causal forces assumption, RBF was one the leading forecasting methods for the annual series while performed very well on the longer horizons for short period series. Results of this competition are available in a 2002 special issue on the M-3 Competition in International Journal of Forecasting.