الفهرس 
Improving forecasting of short-term time series using computational intelligence
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Abstract
Forecasting is one of the important challenges that face decision makers and planners in different fields. Stock market field, in particular, is influenced by economic, political, psychological and even environmental factors. In finance, stock market prediction becomes a necessary operation for investors{u2019} decisions in order to get the maximum return of investments. The fluctuated behavior of the stock indices movements reflects a type of nonlinear time series characterized by uncertainty of the used information. Therefore the process of predicting stock prices is complex and risky. To this end, many classical methods and techniques such as: regression and time series have been introduced to obtain forecasting models. However, such techniques are often not accurate enough for handling uncertain real forecasting problems. Therefore, new forecasting techniques are needed to improve the performance of the currently available forecasting models