The week's pick
Random Articles
Reseach Article
The ANFIS system for Nonlinear Combined Forecasts in the Telecommunications Industry
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 37 - Number 12 |
| Year of Publication: 2012 |
| Authors: Chen-Chun Lin, Chun-Ling Lin, Joseph Z. Shyu, Chin-Teng Lin |
10.5120/4740-6958
|
Chen-Chun Lin, Chun-Ling Lin, Joseph Z. Shyu, Chin-Teng Lin . The ANFIS system for Nonlinear Combined Forecasts in the Telecommunications Industry. International Journal of Computer Applications. 37, 12 ( January 2012), 30-35. DOI=10.5120/4740-6958
Abstract
The Adaptive Network-Based Fuzzy Inference System (ANFIS) has been proven to be efficient for forecasting. To address this concern, this research develops a nonlinear combined forecasting system by ANFIS for predicting the demand of telecommu-nication technology. We investigate the weights assigned to the combined forecast using two linear methods (the Least squares analysis and the Logistic model), as well as one nonlinear me-thods (the Bass model). An empirical data set from 3G technol-ogy development in Taiwan is used to demonstrate the application of the proposed methodology. These results show that the ANFIS method outperforms other individual methods. Also, this proposed work also provides the user with a user interface in which user can fill the query and find the desired forecasting results.
References
- K.Y.Chen, Combining linear and nonlinear model in fore-casting tourism demand, Expert Systems with Applications 38(8), pp.10368-10376, 2011.
- Martino, J.P., Technological forecasting for decision making (3rd ed.). New York: McGraw-Hill, 1993.
- C.C. Holt, Forecasting seasonals and trends by exponen-tially weighted moving averages. International Journal of Forecasting 20(1), pp.5-10, 2004.
- V.A. Banuls, and J.L. Salmeron, A scenario-based assess-ment model-sbam. Technological Forecasting & Social Change 74(6), pp.750-762, 2007.
- V. Mahajan, E. Muller and F.M. Bass, New product diffu-sion models in marketing: A review and directions for re-search, Journal of Marketing Research 54(1), pp.1-26, 1990.
- D.W. Bunn, A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly 26, pp.325–329, 1975.
- K.P Lin and P.F. Pai, A fuzzy support vector regression model for business cycle predictions, Expert Systems with Applications 37(7), pp.5430-5435, 2010.
- K.H. Hassan, Self learning of ANFIS inverse contro; using iterative learning technique, Internation Journal of Computer Applications 21(8), pp.24-29, 2011.
- K.Y. Chen, Combining linear and nonlinear model in forecasting tourism demand, Expert Systems with Applica-tions 38(8), pp.10368-10376, 2011.
- H. Shakouri, R., Nadimi, and F. Ghaderi, A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes, Expert Systems with Applications 36(2), pp.1765-1772, 2009.
- F.M. Tseng and Y.C. Hu, Quadratic-interval Bass model for new product sales diffusion, Expert Systems with Ap-plications 36(4), pp.8496-8502, 2009.
- C.L. Lawson and R.J. Hanson, Solving least squares prob-lems, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1995.
- S.H.Lim, Forecasting models of additional use of mobile digital contents: A comparison of artificial neural networks and logistic regression analysis, International Journal of Computer Science and Network Security 6(6), 146-149, 2006.
- R.L. Chadda and S.S. Chitgopekar, A generalization of the logistic curves and long-range forecasts (1966-1991) of residence telephones, The Bell Journal of Economics and Management Science 2(2), pp.542- 560, 1971.
- D. Chandrasekaran and G.J. Tellis, A critical review of marketing research on diffusion of new products. Review of Marketing Research 3, pp.39-80, 2007.
- D.Talukdar, K. Sudhir and A. Ainslie, Investigating new product diffusion across products and countries, Marketing Science 21(1), pp.97-114, 2002.
- F.M. Bass, A new product growth for model consumer durables, Management science 50(2), pp.1825-1832, 2004.
- J.-S. R. Jang, ANFIS: adaptive-network-based fuzzy infe-rence systems,IEEE Trans. on Systems, Man, and Cyber-netics 23, pp.665-685, 1993
- E. Hadavandi, H. Shavandi, A. Ghanbari, Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting, Knowledge-Based Systems 23 (8), pp.800-808, 2010.
- Ministry of Transportation and Communications (MOTC), Statistical abstract of transportation & communications, available at: www.motc.gov.tw (accessed 11 January 2011), 2010.
Index Terms
Keywords