Advantages and Disadvantages of Using Neural Networks for Predictions

Ciobanu Dumitru () and Vasilescu Maria ()
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Ciobanu Dumitru: University of Craiova
Vasilescu Maria: “Constantin Brâncuºi” University of Târgu Jiu

Ovidius University Annals, Economic Sciences Series, 2013, vol. XIII, issue 1, 444-449

Abstract: Prediction is very important in business planning. The ability to accurately predict the future is fundamental to many decision activities in sales, marketing, production, inventory control, personnel, and many other functional areas of business. Time series modeling approach is one of the major techniques widely used in practice. In general, there are two approaches to modeling and forecasting time series: linear approach and nonlinear approach. Linear models [2] were used for a long time and are still very useful, but linearity assumptions underlying these models may be too restrictive. A nonlinear model more flexible is Artificial Neural Networks (ANN), which have received attention recently [19]. The major advantage of neural networks is that they are data driven and does not require restrictive assumptions about the form of the basic model. In this paper emphasize the strengths and weaknesses of neural networks

Keywords: Time Series; Prediction; Nonlinear models; Artificial Intelligence; Neural Networks. (search for similar items in EconPapers)
JEL-codes: C C53 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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