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RBF consists of 99 rules that were originally published in Collopy and Armstrong (1992). These rules combine forecasts using weights that vary according to features of time series. Four simple methods are combined in RBF:

  • Random walk which emphasizes the short range perspective and assumes there is no trend in the data.
  • Linear regression which represents the long range and provides a basic trend estimate.
  • Holt’s exponential smoothing represents the short range and provides estimate of recent trend.
  • Brown’s exponential smoothing which provides short range estimates.

Since factors impacting the short and long term often differ, RBF rules develop weights for the short and long term forecasts separately and blend these at the end of the run. Rules also include a damping component. Rule 41 from the short-range trend model is presented below:

RULE 41: IF the direction of the basic trend and the direction of the recent trend are not the same OR if the trend agree with one another but differ from causal forces, THEN add 15% to the weight on random walk and subtract it from that on Holt’s and Brown’s

{Explanation: Dissonance calls for conservatism in the trend estimate}

RBF rules are coded as simple production rules in IF… THEN… format with rule conditions being the series features and actions being weight assignments to the four methods listed above.

Corrections to Rule-based Forecasting

Through continued efforts, a revised and corrected set of rules is now available. The corrections to these rules and their impact on performance of RBF are available in the following paper:

Adya, M. (2000). Corrections to rule-based forecasting: findings from a replication. International Journal of Forecasting, 16, 125-128.