IDEAS home Printed from https://ideas.repec.org/a/ovi/oviste/vxiiy2012i1p444-449.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://stec.univ-ovidius.ro/html/anale/ENG/cuprins%20rezumate/volum2013p1.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Mathew Habyarimana & Abayomi A. Adebiyi, 2025. "A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines," Energies, MDPI, vol. 18(7), pages 1-21, March.
    3. 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.
    4. 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.
    5. Mahdi Sedighkia & Asghar Abdoli, 2023. "Design of optimal environmental flow regime at downstream of multireservoir systems by a coupled SWAT-reservoir operation optimization method," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 834-854, January.
    6. 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.
    7. Liu, Jin & Smith, Stephen R., 2020. "A multi-level biogas model to optimise the energy balance of full-scale sewage sludge conventional and THP anaerobic digestion," Renewable Energy, Elsevier, vol. 159(C), pages 756-766.
    8. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    9. 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.
    10. Mahdi Sedighkia & Asghar Abdoli, 2024. "A Simulation–Optimization System to Assess Dam Construction with a Focus on Environmental Degradation at Downstream," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2489-2509, May.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ovi:oviste:v:xii:y:2012:i:1:p:444-449. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gheorghiu Gabriela (email available below). General contact details of provider: https://edirc.repec.org/data/feoviro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.