HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA

REFERENCES

• Abraham , A. 2002 . Intelligent systems: Architectures and perspectives, recent advances in intelligent paradigms and applications . A. Abraham , L. Jain , and J. Kacprzyk . eds., Studies in Fuzziness and Soft Computing , pp. 1 – 35 . Germany : Springer Verlag .
   Google Scholar
• Abraham , A. , C. Grosan , and C. Martin-Vide . 2007 . Evolutionary design of intrusion detection programs . International Journal of Network Security 4 ( 3 ): 328 – 339 .
   Google Scholar
• Abraham , A. and B. Nath . 2001 . A neourofuzzy approach for modeling electricity demand in victoria . Applied Soft Computing Journal 1 : 127 – 138 .
   Google Scholar
• Ali , J. and M. Al-Mahmeed . 2002 . Neural networks: predicting kuwaiti KD currency exchange rates versus U.S dollar . Arab Journal of Administrative Sciences . 6 : 17 – 35 . Rao , C. P. and A. Jafar . Neural network model for database marketing in the new global economy . Marketing Intelligence & Planning 20 ( 1 ): 35 – 43 .
   Google Scholar
• Armstrong , J. S. 2001 . Long Range Forecasting: From Crystal Ball to Computer. (2nd ed.) . New York : Wiley .
   Google Scholar
• Bates , J. M. and C. W. J. Granger . 1969 . The combination of forecasts . Operational Research Quarterly 20 : 451 – 468 .
   Google Scholar
• Besseler , D. A. and J. A. Brandt . 1981 . Forecasting livestocks prices with individual and composite methods . Applied Economics 13 : 513 – 522 .
   Web of Science ®Google Scholar
• Bowermann , B. L. and R. T. O'Connel . 1993 . Forecasting and Time Series: An Applied Approach . Duxbury Pacific Grove , CA , Boston , Duxbury Press .
   Google Scholar
• Box , G. E. P. and G. Jenkins . 1982 . Some practical aspects of forecasting in organization . Journal of Forecasting I pp. 3 – 21 .
   Google Scholar
• Brace , M. C. , J. Schmidt , and M. Hadlin . 1991. Comparison of the forecasting accuracy of neural networks with other established techniques. In: Proceedings of the First Forum on Application of Neural Network to Power Systems . pp. 31–35. Seattle , WA,
   Google Scholar
• Caire , P. , G. Hatabian , and C. Muller . 1992 . Progress in forecasting by neural networks . In: Proceedings of the International Joint Conference on Neural Networks 2 : 540 – 545 .
   Google Scholar
• Chaivivatrakul , S. and S. Somhom . 2004 . Vegetable product forecasting system by adaptive genetic algorithm . TENCON 2004. 2004 IEEE Region 10 Conference , Vol. B , pp. 211 – 214 .
   Google Scholar
• Chatfield , C. 2001 . Time Series Forecasting . Boca-raton , FL : Chapman & Hall/CRC Press .
   Google Scholar
• Chebrolu , S. , A. Abraham , and J. Thomas . 2005 . Feature deduction and ensemble design of intrusion detection systems. Computers and Security . Elsevier Science 24 ( 4 ): 295 – 307 .
   Google Scholar
• Chi , F.-L. 1998 . Forecasting tourism: A combined approach . Tourism Management 19 ( 6 ): 515 – 520 .
   Web of Science ®Google Scholar
• Chiang , B.-C. , S.-L. Tsai , and C.-C. Wang . 2000 . Machine Vision-Based gray relational theory applied to IC marking inspection . IEEE Transactions on Semiconductor Manufacturing 15 ( 4 ).
   Web of Science ®Google Scholar
• Chu , C. W. and G. P. Zhang . 2003 . A comparative study of linear and nonlinear models for aggregates retail sales forecasting . International Journal of Production Economics 86 ( 3 ): 217 – 231 .
   Web of Science ®Google Scholar
• De Gooijer , J. G. and R. J. Hyndman . 2006 . 25 Years of time series forecasting . International Journal of Forecasting 22 : 443 – 473 .
   Web of Science ®Google Scholar
• Delen , D. , G. Waller , and A. Kadam . 2005 . Predicting breast cancer survivability: A comparison of three data mining methods . Artificial Intelligence in Medicine 34 : 113 – 127 .
   PubMed Web of Science ®Google Scholar
• Deng , J. L. 1989 . Introduction to grey system theory . The Journal of Grey System 1 : 1 – 24 .
   Google Scholar
• Deng , J. L. 1982 . Control problems of grey systems . Systems and Control Letters 5 : 288 – 294 .
   Google Scholar
• Denton , J. W. 1995 . How good are neural networks for causal forecasting? The Journal of Business Forecasting 14 ( 2 ): 17 – 20 .
   Google Scholar
• Díaz-Robles , L. A. , J. C. Ortega , J. S. Fu , G. D. Reed , J. C. Chow , J. G. Watson , and J. A. Moncada-Herrera . 2008 . A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas . The Case of Temuco, Chile, Atmospheric Environment 42 ( 35 ): 8331 – 8340 .
   Web of Science ®Google Scholar
• Dorganis , P. , A. Alexandidi , P. Patrinos , and H. Sarinevers . 2006 . Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing . Journal of Food Engineering 75 : 196 – 204 .
   Web of Science ®Google Scholar
• Fu , C. , J. Zheng , J. Zhao , and W. Xu . 2001 . Application of grey relational analysis for corrosion failure of oil tubes . Corrosion Science 43 : 881 – 888 .
   Web of Science ®Google Scholar
• Ganeta , R. , L. M. Romeo , and A. Gill . 2006 . Forecasting of Electricity prices with neural networks . Energy Conversion and Management 47 : 1770 – 1778 .
   Web of Science ®Google Scholar
• Goodwin , P. 2000 . Correct or combine: Mechanically integrating judgmental forecasts with statistical methods . International Journal of Forecasting 16 : 261 – 275 .
   Web of Science ®Google Scholar
• Gorr , W. L. , D. Nagin , and J. Szcypula . 1994 . Comparative study of artificial neural network and statistical models for predicting student grade point averages . International Journal of Forecasting 10 : 17 – 34 .
   Web of Science ®Google Scholar
• Granger , C. W. J. 1993 . Strategies for modeling nonlinear time series relationships . The Economic Record 69 ( 206 ): 233 – 238 .
   Web of Science ®Google Scholar
• Hamid , S. A. and Z. Iqbal . 2004 . Using neural networks for forecasting volatility of S& P 500 index futures prices . Journal of Business Research 57 ( 10 ): 1116 – 1123 .
   Web of Science ®Google Scholar
• Heravi , S. , D. R. Osborn , and R. Birchenhallc . 2004 . Linear versus neural network forecasting for European industrial production series . International Journal of Forecasting 20 : 435 – 446 .
   Web of Science ®Google Scholar
• Hippert , H. S. , C. E. Pedreira , and R. C. Souza . 2001 . Neural networks for short-term load forecasting: A review and evaluation . IEEE Transactions on Power Systems 16 ( 1 ): 44 – 45 .
   Web of Science ®Google Scholar
• Hou , Z. , Z. Lian , Y. Yao , and X. Yuan . 2006 . Cooling-load prediction by the combination of rough set theory & artificial neural network based on data fusion technique . Applied Energy 83 : 1033 – 1046 .
   Web of Science ®Google Scholar
• Jain , A. and A. M. Kumar . 2007 . Hybrid neural network models for hydrologic time series forecasting . Applied Soft Computing 7 ( 2 ): 585 – 592 .
   Web of Science ®Google Scholar
• Jones , S. S. , R. S. Evans , J. L. Allen , A. Thomas , P. J. Haung , S. J. Welch , and G. L. Snow . 2008 . A Multivariate time series approach to modeling and forecasting demand in emergency department . Journal of Biomedical Informatics 4 ( 1 ): 103 – 139 .
   Google Scholar
• Kang , S. 1991 . An investigation of the used of feedforward neural networks for forecasting . PhD thesis . Ohio : Kent State University , Kent, OH .
   Google Scholar
• Lachtermacher , G. and J. D. Fuller . 1995 . Backpropagation in time series forecasting . Journal of Forecasting 14 : 381 – 393 .
   Web of Science ®Google Scholar
• Li , Y. , A. Li , R. Li , L. Wei , and S. Qin . 2003. Study on characteristics of biomass pyrolysis gas in view of grey relation analysis and BP neural network. ACTA Energies Solaris Sinica 24(6):776–780.
   Google Scholar
• Lin , S. J. , I. J. Lu , and C. Lewis . 2007 . Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan . Energy Policy 35 : 1948 – 1955 .
   Web of Science ®Google Scholar
• Lu , J.-C. , D.-X. Niu , and Z.-L. Jia . 2004 . A study of short term load forecasting based on ARIMA-ANN. Proceedings of the Third International Conference on Machine Learning and Cybernetics. August, pp. 26–29. Shanghai. .
   Google Scholar
• Ma , L. and K. Khorasani . 2004 . New training strategies for constructive neural networks with application to regression problem . Neural Network 17 : 589 – 609 .
   PubMed Web of Science ®Google Scholar
• Makridakis , S. and S. C. Wheelwright . 1989 . Forecasting Methods for Management. , 5th ed. , New York : John Wiley and Sons .
   Google Scholar
• Makridakis , S. 1993 . Accuracy measures: Theoretical and practical concerns . International Journal of Forecasting 9 : 527 – 529 .
   Web of Science ®Google Scholar
• Makridakis , S. , A. Anderson , R. Carbone , R. Fildes , M. Hibdon , R. Lewandowski , J. Newton , E. Parzen , and R. Winkler . 1982 . The accuracy of extrapolation (time series) methods: Results of a forecasting competition . Journal of Forecasting 1 : 111 – 153 .
   Web of Science ®Google Scholar
• McLeod , A. I. and W. K. Li . ( 2004 ). Diagnostic checking ARMA time series model using squared residual autocorrelations . Journal of Time Series Analysis 4 : 269 – 273 . In: S. Heravi, D.-R. Osborn, and C. R. Birchenhall, eds. Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting 20:435–446.
   Google Scholar
• Mentzer , J. T. and C. C. Bienstock . 1998 . Sales Forecasting Management . Thousand Oaks , CA : Sage Publications .
   Google Scholar
• Newbold , P. and C. W. J. Granger . 1974 . Experience with forecasting univariate time series and combination of forecast . Journal of the Royal Statistical Society Series A 137 : 131 – 149 .
   Web of Science ®Google Scholar
• Nikolopoulos , K. , P. Goodwin , A. Patelis , and V. Assimakopoulos . 2007 . Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches . European Journal of Operational Research 180 : 354 – 368 .
   Web of Science ®Google Scholar
• Pai , P. F. and C.-S. Lim . 2005 . A hybrid ARIMA and support vector machines model in stock price forecasting . Omega 33 : 497 – 505 .
   Web of Science ®Google Scholar
• Parzen , E. and R. Winkler . 1982 . The accuracy of extrapolation (time series) methods: Results of a forecasting competition . Journal of Forecasting 1 : 111 – 153 .
   Web of Science ®Google Scholar
• Prerez , S. M. , M. H. Ortega , M. L. Gonzalez , and Z. Boger . 2004 . Comparative study of artificial neural network and multivariate methods to classify spanish do rose wines . Talanta 62 : 983 – 990 .
   PubMed Web of Science ®Google Scholar
• Rao , C. P. and J. Ali . 2002 . Neural network model for database marketing in the new global economy . Marketing Intelligence & Planning 20 ( 1 ): 35 – 43 .
   Google Scholar
• Reid , D. J. 1968 . Combining three estimates of gross domestic product . Economica 35 : 431 – 444 .
   Web of Science ®Google Scholar
• Sallehuddin , R. , M. Shamsuddin , and N. M-Yusof . 2008a . Artificial neural network time series modeling for revenue forecasting . Chiang Mai Journal of Science 35 ( 1 ): 1 – 16 .
   Google Scholar
• Sallehuddin , R. , S. M. Shamsuddin , and S. Z. M. Hashim . 2008b . Application of grey relational analysis for multivariate time series . Eight International Conference on Int. System Design and Application pp. 432–437. .
   Google Scholar
• Shamsuddin , M. N. 1992 . A short note on forecasting natural rubber prices using a MARMA . The Malaysian Journal of Agricultural Economic 9 : 59 – 68 .
   Google Scholar
• Shamsuddin , M. N. and F. M. Arshad . 1990 . Composite model for short term forecasting for natural rubber prices . Pertanika 12 ( 2 ): 283 – 288 .
   Google Scholar
• Shouyang , W. , W. Y. Lean , and K. K. Lai . 2005 . Crude oil price forecasting with TEI@Imethodology . Journal of Systems Science and Complexity 18 ( 2 ): 145 – 166 .
   Google Scholar
• Srinivasulu , S. and A. Jain . 2006 . A comparative analysis of training methods for ANN rainfall-runoff models . Applied Soft Computing 6 : 295 – 306 .
   Web of Science ®Google Scholar
• Taskaya-Termizel , T. and M. C. Casey . 2005 . A comparative study of autoregressive neural network hybrids . Neural Networks 18 ( 5–6 ): 781 – 789 .
   PubMed Web of Science ®Google Scholar
• Taskaya-Termizel , T. and K. Ahmad 2005 . Are ARIMA neural network hybrids better than single models? . In: Proceeding of International Joint Conference On NNs (IJCN 2005) . Montreal , Canada .
   Google Scholar
• Taylor , J. W. 2003 . Short term electricity demand forecasting using double seasonal exponential smoothing . Journal of The Operational Research Society 54 : 799 – 805 .
   Web of Science ®Google Scholar
• Tosun , N. 2005 . Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis . International Journal of Advance Manufacturing Technology 28 ( 5–6 ): 400 – 455 .
   Web of Science ®Google Scholar
• Tseng , F.-M. , H.-C. Yu , and G.-H. Tzeng . 2002. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change 69:71–87.
   Web of Science ®Google Scholar
• Uysal , M. and M. Roubi . 1999 . Artificial neural network versus multiple regression in tourism demand analysis . Journal of Travel Research 38 : 111 – 118 .
   Google Scholar
• Voort , V. D. , M. Dougherty , and M. Watson . 2005 . Combining Kohonen maps with ARIMA time series models to forecast traffic flow . Transp. Resc. Circ. (Emerge Techno) (1996) 4C(5):307–308; 8:18–21, August, 4764–4769. .
   Google Scholar
• Xiao , Z. , S.-J. Ye , B. Zhang , and C.-S. Sun . 2007 . BP neural network with rough set for short term load forecasting . Expert Systems with Applications 36 ( 1 ): 273 – 279 .
   Web of Science ®Google Scholar
• Yang , H. Y. , H. Ye , G. Wang , J. Khan , and T. Hu . 2006 . Fuzzy neural very short term load forecasting based on chaotic dynamics reconstruction . Chaos, Solitons & Fractals 29 : 462 – 469 .
   Web of Science ®Google Scholar
• Young , P. 1993 . Technological growth curves – A comparison of forecasting model . Technological Forecasting and Social Change 44 : 375 – 389 .
   Web of Science ®Google Scholar
• Zhang , G. P. 2003 . Time series forecasting using a hybrid ARIMA and neural network model . Neurocomputing 50 : 159 – 175 .
   Web of Science ®Google Scholar
• Zhang , G. P. and M. Qi . 2005 . Neural network forecasting for seasonal and trend time series . European Journal of Operation Research 160 : 501 – 504 .
   Web of Science ®Google Scholar
• Zhang , G. P. 2004 . Neural Networks in Business Forecasting . London , UK , Idea Group Publishing Press .
   Google Scholar
• Zhang , G. P. , B. E. Patuwo , and M. Y. Hu . 1998 . Forecasting with artificial neural networks. The state of the art . International Journal of Forecasting 14 ( 1 ): 35 – 62 .
   Web of Science ®Google Scholar
• Zhang , G. P. , B. E. Patuwo , and M. Y. Hu . 2001 . A simulation study of artificial neural networks for nonlinear time-series forecasting . Computers & Operations Research 28 : 381 – 396 .
   Web of Science ®Google Scholar
• Zhang , Y. and Y. He . 2005 . Study of prediction model on grey relational BP neural network based on rough set . In: Proceedings of The Fourth International Conference on Machine Learning and Cybernetics . Guangzhou , China , pp. 4764 – 4769 .
   Google Scholar
• Zou , H. F. , G. P. Xia , F. T. Yang , and H. Y. Wang . 2007 . An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting . Neurocomputing , 70 ( 16–18 ): 2913 – 2923 . doi:10.1016/j.neucom.2007.01.009. .
   Web of Science ®Google Scholar