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http://dx.doi.org/10.5351/KJAS.2005.18.1.137

Time Series Forecasting Based on Modified Ensemble Algorithm  

Kim Yon Hyong (Department of Data information, Jeonju University)
Kim Jae Hoon (Department of Liberal Arst, Jeonju University)
Publication Information
The Korean Journal of Applied Statistics / v.18, no.1, 2005 , pp. 137-146 More about this Journal
Abstract
Neural network is one of the most notable technique. It usually provides more powerful forecasting models than the traditional time series techniques. Employing the Ensemble technique in forecasting model, one should provide a initial distribution. Usually the uniform distribution is assumed so that the initialization is noninformative. However, it would be expected a sequential informative initialization based on data rather than the uniform initialization gives further reduction in forecasting error. In this note, a modified Ensemble algorithm using sequential initial probability is developed. The sequential distribution is designed to have much weight on the recent data.
Keywords
Neural network; Initial distribution; Sequential initial; Modified ensemble algorithm;
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