SELECTING AND COMBINING ROBUST FORECASTING MODELS USING RULES BASED ON VALIDITY AND RELIABILITY

A new approach to combining forecasts by rules based on reliability and validity is proposed in terms of the basic principle of the personnel scrutiny method. Robust models that fit in current forecasting problems are chosen by means of matching rules based on reliability and validity, forming the participating model sets of historical case set and current case set. Then, these models are assigned the degrees of satisfaction according to their forecasting accuracy on the above-mentioned two case sets and endowed with corresponding weights. A rational experiment on validating the combination model is designed. The result shows the proposed method not only attains a forecasting accuracy comparable to that of the theoretically optimal combining methods but also provides some valuable insights into combination methodology, which verifies that the combining forecast upgrades the forecasting performance.