Forecasting in a complex environment using feature manipulating technique added in traditional forecasting system

Most of forecasting systems is composed of two modules: preprocessing module and learning module. In preprocessing module basic operations such as the removal of noise or outliners are performed. In learning module the knowledge contained in training data is obtained. Many forecasting systems are applicable in a simple or simplified environment and work well yet have weak points when applied in a complex environment. That results from the characteristics of the features of training data are changed in response to training data; i.e., corresponding to the patterns of data the degrees of the influences of the features, which are subset of attributes or weighted sum of attributes, are changed. We present a more advanced forecasting system to apply in a complex environment.