Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Sales Prediction of Electronic Appliances using a Convergence Model based on Artificial Neural Network and Genetic Algorithm

인공신경망과 유전자 알고리즘 기반의 융합모델을 이용한 가전제품의 판매예측

  • Received : 2015.07.13
  • Accepted : 2015.09.20
  • Published : 2015.09.28
Download PDF
⟨ Previous Next ⟩

Abstract

The brand and product awareness of Korean electronics companies in the North American market has grown significantly and North American consumers has been recognized as an innovative technology products good performance of Korean electronics appliances. The consumer need of energy saving has led to a rise in market share because Korean electronics appliances have the excellence in energy saving aspects. The expansion of smartphones and mobile devices and the development of smart grid technology can affect electronics market. Domestic companies are continuously develop new product to provide consumers convenient with a variety of additional features combined consumer products. This study proposes a convergence model for sales prediction of electronic appliances using sales data of A company from the North American market. We develop the convergence model for sales prediction based on based on artificial neural network and genetic algorithm. In addition, we validate the superiority of the proposed convergence model by comparing the prediction performance of traditional prediction models.

북미시장에서 국내 가전업체의 브랜드 및 제품 인지도는 크게 성장했으며 북미 소비자들에게 국내 기업의 제품은 성능이 좋고 혁신적인 기술 제품으로 인식되고 있다. 또한 에너지 절약을 원하는 소비자가 늘어나면서 국내 가전제품의 에너지 절약 측면에서 우수성이 부각됨에 따라 시장점유율이 상승으로 이어지고 있다. 최근 스마트폰과 모바일 기기 시장 확대 및 스마트 그리드 기술 발달의 영향으로 가전제품 시장에도 스마트 열풍이 거세게 몰아치고 있는데, 국내 기업들은 가전제품과 결합된 다양한 부가기능을 통해 소비자 편의를 제공함에 따라 지속적인 제품개발을 하고 있다. 본 연구에서는 지속적인 경쟁우위를 유지하기 위한 방안으로 국내 A사의 북미시장에서의 TV 판매 데이터를 이용하여 북미시장에서의 가전제품 판매예측을 위한 융합모델을 개발하고자 한다. 본 연구에서는 인공신경망과 유전자 알고리즘 기반의 융합모델을 이용한 가전제품의 판매예측을 수행하기로 한다. 추가적으로 본 연구에서는 제안한 융합모델과 기존의 예측모델과의 비교분석을 통해 제안한 융합모델의 우수성을 입증하기로 한다.

Keywords

References

  1. C. -W. Chu, G. P. Zhang, A Comparative Study of Linear and Nonlinear Models for Aggregate Retail Sales Forecasting, International Journal of Production Economics, Vol. 86, No. 3, pp. 217-231, 2003. https://doi.org/10.1016/S0925-5273(03)00068-9
  2. J. H. Park, K. -K. Seo, Approximate Life Cycle Assessment of Product Concepts using Multiple Regression Analysis and Artificial Neural Networks, KSME International Journal, Vol. 17, No. 12, pp 1969-1976, 2003. https://doi.org/10.1007/BF02982436
  3. Z. Guoqiang, B. E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting, Vol. 14, No. 1, pp. 35-62, 1998. https://doi.org/10.1016/S0169-2070(97)00044-7
  4. D. C. Park, M.A. El-Sharkawi, R.J. Marks, L.E. Atlas, Electric load forecasting using an artificial neural network, IEEE Transactions on Power Systems, Vol. 6, No.2. pp. 442-449, 1991.
  5. K. Iebeling, B. Milton, Designing a neural network for forecasting financial and economic time series, Neurocomputing, Vol. 10, No. 3, pp. 215-236, 1996. https://doi.org/10.1016/0925-2312(95)00039-9
  6. J. -H. Lee, J. -S. Kim, H. -W. Jang, J. -C. Lee, Drought Forecasting Using the Multi Layer Perceptron (MLP) Artificial Neural Network Model, Journal of Korea Water Resources Association, Vol. 46, No. 12, pp. 1249-1263, 2013. https://doi.org/10.3741/JKWRA.2013.46.12.1249
  7. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors, Nature, Vol. 323, pp. 533-536, 1986. https://doi.org/10.1038/323533a0
  8. W. K. Yeo, Y. M. Seo, S. Y. Lee, H. K. Jee, Study on Water Stage Prediction Using Hybrid Model of ANN and GA, Journal of Korea Water Resources Association, Vol. 43, No. 8, pp. 721-731, 2010. https://doi.org/10.3741/JKWRA.2010.43.8.721
  9. Z. Michalewicz, Genetic algorithms + data structures=evolution programs (3rd ed.), Springer-Verlag, 1996.
  10. D. T. Pham, G. Jin, Genetic algorithm using gradient-like reproduction operator, Electronics Letters, Vol. 31, No. 18, pp. 1558-1559, 1995. https://doi.org/10.1049/el:19951092
  11. D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, 1989.
  12. V. Maniezzo, Genetic evaluation of the topology and weight distribution of neural network, IEEE Transaction of Neural Network, Vol. 5, No. 1, pp.39-53, 1994. https://doi.org/10.1109/72.265959
  13. M. Nasseri, K. Asghari, M.J. Abedini, Optimized scenario for rainfall forecasting using GA coupled with ANN, Expert Systems with Applications, Vol. 35, No. 3, pp. 1415-1421, 2008. https://doi.org/10.1016/j.eswa.2007.08.033
  14. K. -K. Seo, Development of a Sales Prediction Model of Electronic Appliances using Artificial Neural Networks, Journal of Digital Convergence, Vol. 12, No. 11, pp. 209-214, 2014. https://doi.org/10.14400/JDC.2014.12.11.209
  15. K. -K. Seo, An Application of One-class Support Vector Machines in Content-based Image Retrieval, Expert Systems with Applications, Vol. 33, No. 2, pp. 491-498, 2007. https://doi.org/10.1016/j.eswa.2006.05.030