I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods....

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six U.S. Presidential elections from 1992 through 2012, we then report the reduction in error obtained by averaging forecasts within and...

To improve forecasting accuracy, combine forecasts derived from methods that differ substantially and draw from different sources of information. When feasible, use five or more methods. Use formal procedures to combine forecasts: An equal-weights rule offers a reasonable starting point, and a...

In 1982, the Journal of Forecasting published the results of a forecasting competition organized by Spyros Makridakis (Makridakis et al., 1982). In this, the ex ante forecast errors of 21 methods were compared for forecasts of a variety of economic time series, generally using 1001 time series....

This paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The...

Rule-based forecasting (RBF) is an expert system that uses features of time series to select and weight extrapolation techniques. Thus, it is dependent upon the identification of features of the time series. Judgmental coding of these features is expensive and the reliability of the ratings is...

The M3-Competition continues to improve the design of forecasting competitions: It examines more series than any previous competition, improves error analyses and includes commercial forecasting programs as competitors. To judge where to go from here, I step back to look at the M-Competitions as...

Causal forces are a way of summarizing forecasters' expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting...

Many researchers appear to operate under the impression that causal models lead to more accurate forecasts than those provided by naive models (or “projections”). This study was based on the premise that causal models lead to better forecasts than do naive models in certain situations. The...

The field of forecasting is concerned with making statements about matters that are currently unknown. The terms "forecast," "prediction," "projections," and "prognosis" are interchangeable as commonly used. Forecasting is also concerned with the effective presentation and use of forecasts