Time series forecasting by a seasonal support vector regression model

Expert Systems with Applications

Volumn 37, Issue 6, 2010, Pages 4261-4265

  Pai, Ping Feng ^a   Lin, Kuo Ping ^b   Lin, Chi Shen ^c   Chang, Ping Teng ^c  

^a NATIONAL CHI NAN UNIVERSITY   (Taiwan)

^b LUNGHWA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c TUNGHAI UNIVERSITY   (Taiwan)

Author keywords

Forecast; Seasonal autoregressive integrated moving average; Seasonal time series; Support vector regression

Indexed keywords

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; DATA SETS; EMPIRICAL RESULTS; FORECASTING ACCURACY; FORECASTING MODELS; HYBRID GENETIC ALGORITHMS; SEASONAL FACTORS; SEASONAL TIME SERIES; SUPPORT VECTOR REGRESSION MODELS; SUPPORT VECTOR REGRESSIONS; THREE PARAMETERS; TIME SERIES FORECASTING;

REGRESSION ANALYSIS; TABU SEARCH; TIME SERIES; VECTORS;

FORECASTING;

EID: 77249122037     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.11.076     Document Type: Article

References (26)

1. Box G., and Jenkins G. Time series analysis: Forecasting and control (1976), Holden-Day, San Francisco
2. Gaoa C., Bompard E., Napoli R., and Cheng H. Price forecast in the competitive electricity market by support vector machine. Physica A 382 (2007) 98-113
3. Glover F., Kelly J.P., and Laguna M. Genetic algorithms and tabu search: hybrids for optimization. Computers and Operations Research 22 (1995) 111-134
4. Goh C., and Law R. Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management 23 (2002) 499-510
5. Holland J.H. Adaptation in natural and artificial system (1975), University of Michigan Press, Ann Arbor
6. Hong W.C., and Pai P.F. Potential assessment of the support vector regression technique in rainfall forecasting. Water Resources Management 21 (2007) 495-513
7. Huang J.-H., and Min J.C.-H. Earthquake devastation and recovery in tourism: The Taiwan case. Tourism Management 23 (2002) 145-154
8. Huang W., Nakamori Y., and Wang S.Y. Forecasting stock market movement direction with support vector machine. Computer and Operations Research 32 (2005) 2513-2522
9. Hua Z.S., and Zhang B. A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation 181 (2006) 1035-1048
10. Kim K.J. Financial time series forecasting using support vector machines. Neurocomputing 55 (2003) 307-319
11. Levis A.A., and Papageorgiou L.G. Customer demand forecasting via support vector regression analysis. Chemical Engineering Research and Design 83 (2005) 1009-1018
12. Li T., Campbell E.P., Haswell D., Sneeuwjagt R.J., and Venables W.N. Statistical forecasting of soil dryness index in the southwest of western Australia. Forest Ecology and Management 183 (2003) 147-157
13. Lu W.Z., and Wang W.J. Potential assessment of the "Support Vector Machine" method in forecasting Ambient air pollutant trends. Chemosphere 59 (2005) 693-701
14. Makridakis S., Wheelwright S.C., and Hyndman R.J. Forecasting: Methods and applications (1998), Wiley, New York
15. Mercer J. Function of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A 209 (1909) 415-446
16. Mohandes M.A., Halawani T.O., Rehman S., and Hussain A.A. Support vector machines for wind speed prediction. Renewable Energy 29 (2004) 939-947
17. Nam K., and Schaefer T. Forecasting international airline passenger traffic using neural networks. Logistics and Transportation Review 31 (1995) 239-251
18. Navarro-Esbri{dotless}́ J., Diamadopoulos E., and Ginestar D. Time series analysis and forecasting techniques for municipal solid waste management. Resources, Conservation and Recycling 35 (2002) 201-214
19. Pai P.F., and Hong W.C. An improved neural network model in forecasting tourist arrivals. Annals of Tourism Research 32 (2005) 1138-1141
20. Tang Z., and Fishwick P.A. Feedforward neural nets as models for time series forecasting. ORSA Journal of Computing 5 (1993) 374-385
21. Tay F.E.H., and Cao L. Modified support vector machines in financial time series forecasting. Neurcomputing 48 (2002) 847-861
22. Vapnik V., Golowich S., and Smola A. Support vector machine for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9 (1996) 281-287
23. Williams, P. M. (1997). Modelling seasonality and trends in daily rainfall data. In: D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems (Vol. 10, pp. 985-991).
24. Xi X.C., Poo A.N., and Chou S.K. Support vector regression model predictive control on a HVAC plant. Control Engineering Practice 15 (2007) 897-908
25. Yu P.S., Chen S.T., and Chang I.F. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328 (2006) 704-716
26. Zhang G., and Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 160 (2005) 501-514