An enhanced hybrid method for time series prediction using linear and neural network models

Applied Intelligence

Volumn 37, Issue 4, 2012, Pages 511-519

  Purwanto, ^a,b   Eswaran, C ^a   Logeswaran, R ^a  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b DIAN NUSWANTORO UNIVERSITY   (Indonesia)

Author keywords

ARIMA; Enhanced hybrid method; Exponential smoothing; Linear regression; Neural network

Indexed keywords

FORECASTING; LINEAR REGRESSION; NEURAL NETWORKS;

ARIMA; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; EFFICIENT PREDICTIONS; ENHANCED HYBRID METHODS; EXPONENTIAL SMOOTHING; NON-LINEAR RELATIONSHIPS; NONLINEAR COMBINATION; TIME SERIES PREDICTION;

TIME SERIES;

EID: 84859207448     PISSN: 0924669X     EISSN: 15737497     Source Type: Journal    
DOI: 10.1007/s10489-012-0344-1     Document Type: Article

References (20)

1. Javier C, Rosario E, Francisco JN, Conejo AJ (2003) ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18: 1014-1020
2. Katimon A, Demun AS (2004) Water use trend at universiti teknologi Malaysia: application of ARIMA model. J Pendidik Univ Teknol Malays 41: 47-56
3. Moghaddas-Tafreshi SM, Farhadi M (2008) Linear regressionbased study for temperature sensitivity analysis of Iran electrical load. In: IEEE international conference on industrial technology, pp 1-7
4. Li BJ, Hua HC (2007) The combined forecasting method of GM(1,1) with linear regression and its application. In: International conference on grey systems and intelligent services, pp 394-398
5. Sitte R (2002) Neural networks approach to the random walk dilemma of financial time series. Appl Intell 16: 163-171
6. Neukukar V, Hamidi-Beheshti MT (2010) A local linear radial basis function neural network for financial time series forecasting. Appl Intell 33: 352-356
7. Harri N, Teri H, Ari K, Juhani R, Kolehmaine M (2004) Evolving the neural network model for forecasting air pollution time series. Eng Appl Artif Intell 17: 159-167
8. Faruk DO (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23: 586-594
9. Santoso S (2009) Business forecasting: metode peramalan bisnis masa kini dengan minitab dan SPSS. Elex Media Komputindo, Jakarta
10. Le CT (2003) Introductory biostatistics. Wiley, New York
11. Peter JB, Davis RA (2002) Introduction to time series and forecasting. Springer, New York
12. Suhartono (2008) Feedforward neural networks untuk pemodelan runtun waktu. Dissertation, Gajah Mada University, Yogyakarta, Indonesia
13. Denton JW (1995) How good are neural networks for causal forecasting? J Bus Forecast 14: 17-20
14. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159-175
15. Roselina S, Siti MS, Hashim SZM (2008) Hybridization model of linear and nonlinear time series data for forecasting. In: Second Asia international conference on modelling & simulation, pp 597-602
16. Kim TH, Lee YS, Newbold P (2004) Spurious nonlinear regressions in econometrics. School of Economics, University of Nottingham
17. World Health Organization (2009) Global tuberculosis control: epidemiology, strategy, financing: WHO report 2009. WHO Press, Geneva
18. Hyndman RJ (n.d.) (2011) Time series data library. http://robjhyndman.com/TSDL. Accessed on October 10, 2011
19. Rojas I, Valenzuela O, Rojas F, Guillen A, Herrera LJ, Pomares H et al (2008) Soft-computing techniques and ARMA model for time series prediction. Neurocomputing 71: 519-537
20. Lee YS, Tong LI (2011) Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl-Based Syst 24: 66-72