Forecasting time series using logical combinations of neural-based networks
Arit Thammano · 2002
Forecasts are the basis for planning and decision making. The more accurate the organization's forecasts, the better prepared it will be to take advantage of future opportunities and to reduce potential risks. Thus, it should come as no surprise that there is a tremendous number of statistical prediction algorithms already in existence. However, most of them are useful only when the time series exhibit little trend or seasonal variations but a great deal of irregular or random variation. Therefore, the objective of this paper is to propose a new intelligent forecasting technique which is constructed by combining n-trained neural-based network together. The experimental results based on simulated data show a significant improvement in forecasting accuracy.