# Combination of Forecasts in Data Mining

Chi Kin Chan (The Hong Kong Polytechnic University, Hong Kong)
DOI: 10.4018/978-1-60566-026-4.ch096
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## Abstract

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts. Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that combining multiple forecasts leads to increased forecast accuracy. This has been the result whether the forecasts are judgmental or statistical, econometric or extrapolation. Furthermore, in many cases one can make dramatic performance improvements by simply averaging the forecasts.
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## Background Of Combination Of Forecasts

The concept of combining forecasts started with the seminal work 37 years ago of Bates and Granger (1969). Given two individual forecasts of a time series, Bates and Granger (1969) demonstrated that a suitable linear combination of the two forecasts may result in a better forecast than the two original ones, in the sense of a smaller error variance. Table 1 shows an example in which two individual forecasts (1 and 2) and their arithmetic mean (combined forecast) were used to forecast 12 monthly data of a certain time series (actual data).

Table 1.
Individual and combined forecasts
 Actual data(monthly data) IndividualForecast1 IndividualForecast2 Combined Forecast(Simple Average of Forecast 1 and Forecast 2) 196 195 199 197 196 190 206 198 236 218 212 215 235 217 213 215 229 226 238 232 243 260 265 262.5 264 288 254 271 272 288 270 279 237 249 248 248.5 211 220 221 220.5 180 192 192 192 201 214 208 211

## Key Terms in this Chapter

Data Mining: The process of selection, exploration, and modeling of large quantities of data to discover regularities or relations that are at first unknown with the aim of obtaining clear and useful results for the owner of the database.

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