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Advantages and Disadvantages of Using Neural Networks for Predictions

Author

Listed:
  • Ciobanu Dumitru

    (University of Craiova)

  • Vasilescu Maria

    (“Constantin Brâncuºi” University of Târgu Jiu)

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

Suggested Citation

  • Ciobanu Dumitru & Vasilescu Maria, 2013. "Advantages and Disadvantages of Using Neural Networks for Predictions," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 444-449, May.
    • Handle: RePEc:ovi:oviste:v:xii:y:2012:i:1:p:444-449
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    References listed on IDEAS

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    1. Anonymous, 1949. "Council of Europe," International Organization, Cambridge University Press, vol. 3(4), pages 729-732, November.
    2. Anonymous, 1949. "Council of Europe," International Organization, Cambridge University Press, vol. 3(3), pages 551-552, August.
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    2. Jiachuang Wang & Haoji Ma & Xianhang Yan, 2023. "Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms," Mathematics, MDPI, vol. 11(4), pages 1-29, February.
    3. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
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    5. Ravita D. Prasad, 2024. "School Electricity Consumption in a Small Island Country: The Case of Fiji," Energies, MDPI, vol. 17(7), pages 1-25, April.
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    7. Fuping Liu & Ying Liu & Chen Yang & Ruixun Lai, 2022. "A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4785-4797, September.
    8. Shaghayegh Rahnama & Adriana Cortez & Andres Monzon, 2024. "Navigating Passenger Satisfaction: A Structural Equation Modeling–Artificial Neural Network Approach to Intercity Bus Services," Sustainability, MDPI, vol. 16(11), pages 1-33, May.

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    More about this item

    Keywords

    Time Series; Prediction; Nonlinear models; Artificial Intelligence; Neural Networks.;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C - Mathematical and Quantitative Methods

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