UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf’s sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority.
|
| References |
[1] N. Siti Mariam, Y. Fadhila, L.K. Ibrahim, Meteorilogical multivariable approximation and prediction with classical VAR-DCC approach, Sains Malaysiana 47 (2) (2018) 409–417. [2] L. Muhamad Safiih, Z. Nurul Hila, A. Mohd Tajuddin, P. Vigneswary, R. Mohd Noor Afiq, Z. Razak, I. Md Suffian, K. Idham, Improving the performance of ANN-ARIMA models for predicting water quality at the Kuala Terengganu offshore, Terengganu, Malaysia, J. Sustain. Sci. Manage. 13 (1) (2018) 27–37. [3] H.B. Hwang, Insights into neural-network forecasting time series corresponding to ARMA (p, q) structures, Omega 29 (2001) 273–289. [4] A. Chen, M.T. Leung, D. Hazem, Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan stock index, Comput. Oper. Res. 30 (2003) 901–923. [5] G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (2003) 159–175. [6] A. Timmermann, C.W.J. Granger, Efficient market hypothesis and forecasting, Int. J. Forecast. 20 (2004) 15–27. [7] Y. Chen, B. Yang, J. Dong, A. Abraham, Time-series forecasting using flexible neural tree model, Inform. Sci. 174 (3–4) (2005) 219–235. [8] P.F. Pai, C.S. Lin, A hybrid ARIMA and support vector machines model in stock price forecasting, Omega 33 (2005) 505–597. [9] M. Khashei, Forecasting the Esfahan Steel Company Production Price in Tehran Metals Exchange using Artificial Neural Networks (ANNs) (Master of Science thesis), Isfahan University of Technology, Iran, 2005. [10] K.Y. Chen, C.H. Wang, A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Syst. Appl. 32 (2007) 254–264. [11] F. Giordano, M. La Rocca, C. Perna, Forecasting nonlinear time series with neural network sieve bootstrap, Comput. Statist. Data Anal. 51 (2007) 3871–3884. [12] Z.J. Zhou, C.H. Hu, An effective hybrid approach based on grey and ARMA for forecasting gyro drift, Chaos Solitons Fractals 35 (2008) 525–529. [13] I. Mohd Zamri, R. Zailan, M. Ismail, L. Muhamad Safiih, Forecasting and time series analysis of air pollutants in several area of Malaysia, Am. J. Environ. Sci. 5 (5) (2009) 625–632. [14] I. Mohd Zamri, R. Zailan, M. Ismail, L. Muhamad Safiih, Time-series analysis of pollitants in east coast peninsular Malaysia, J. Sustain. Sci. Manage. 5 (1) (2010) 57–65. [15] M. Khashei, M. Bijari, A new hybrid methodology for nonlinear time series forecasting, Model. Simul. Eng. 2011 (2010) 1–6. [16] X. Wang, M. Meng, A hybrid neural network and ARIMA model for energy consumption forecasting, J. Comput. 7 (5) (2012) 1184–1190. [17] S.D. Balkin, J.K. Ord, Automatic neural network modeling for univariate time series, Int. J. Forecast. 16 (2000) 509–515. [18] A. Jain, A.M. Kumar, Hybrid neural network models for hydrologic time series forecasting, Appl. Soft Comput. 7 (2007) 585–592. [19] P. Jiang, F. Liu, Y. Song, A hybrid forecasting model based on dateframework strategy and improved feature selection technology for short-term load forecasting, Energy 119 (2017) 694–709. [20] J. Wang, F. Liu, Y. Song, J. Zhao, A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system, Appl. Soft Comput. 48 (2016) 281–297. [21] L. Yu, S. Wang, K.K. Lai, A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates, Comput. Oper. Res. 32 (2005) 2523–2541. [22] G. Armano, M. Marchesi, A. Murru, A hybrid genetic-neural architecture for stock indexes forecasting, Inform. Sci. 170 (2005) 3–33. [23] H. Kim, K. Shin, A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets, Appl. Soft Comput. 7 (2007) 569–576. [24] S. Qin, F. Liu, J. Wang, B. Sun, Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models, Atmos. Environ. 98 (2014) 665–675. [25] M. Khashei, S.R. Hejazi, M. Bijari, A new hybrid artificial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems 159 (2008) 769–786. [26] S.K. Nanda, D.P. Tripathy, S.K. Nayak, S. Mohapatra, Prediction of rainfall in India using artificial neural network (ANN) models. I, J. Intell. Syst. Appl. 12 (2013) 1–22. [27] K.W. Kajitani, A.I. Mcleod, Forecasting nonlinear time series with feedforward neural networks: a case study of Canadian lynx data, J. Forecast. 24 (2) (2005) 105–117. [28] G.P. Zhang, A neural network ensemble method with jittered training data for time series forecasting, Inform. Sci. 177 (2007) 5329–5346. [29] M. Khashei, M. Bijari, An artificial neural network (p,d,q) model for time series forecasting, Expert Syst. Appl. 37 (2010) 479–489. [30] M. Khashei, M. Bijari, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Appl. Soft Comput. 11 (2011) 2664–2675. [31] M.C. Medeiros, A. Veiga, A hybrid linear-neural model for time series forecasting, IEEE Trans. Neural Netw. 11 (6) (2000) 1402–1412. [32] F.M. Tseng, H.C. Yu, G.H. Tzeng, Combining neural network model with seasonal time series ARIMA model, Technol. Forecast. Soc. Change 69 (2002) 71–87. [33] G.P. Zhang, G.M. Qi, Neural network forecasting for seasonal and trend time series, European J. Oper. Res. 160 (2005) 501–514. [34] T. Taskaya, M.C. Casey, A comparative study of autoregressive neural network hybrids, Neural Netw. 18 (2005) 781–789. [35] V.L. Berardi, G.P. Zhang, An empirical investigation of bias and variance in time series forecasting: Modeling considerations and error evaluation, IEEE Trans. Neural Netw. 14 (3) (2003) 668–679. [36] M. Ghiassi, H. Saidane, A dynamic architecture for artificial neural networks, Neurocomputing 63 (2005) 397–413. [37] G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: The state of the art, Int. J. Forecast. 14 (1998) 35–62. [38] E. Pelikan, C. de Groot, D. Wurtz, Power consumption in west-bohemia: Improved forecasts with decorrelating connectionist networks, Neural Netw. World 2 (1992) 701–712. [39] I. Ginzburg, D. Horn, Combined neural networks for time series analysis, Adv. Neural Inf. Process. Syst. 6 (1994) 224–231. [40] D.K. Wedding, K.J. Cios, Time series forecasting by combining networks, certainty factors, RBF and the Box–Jenkins model, Neurocomputing 10 (1996) 149–168. [41] J.T. Luxhoj, J.O. Riis, B. Stensballe, A hybrid econometric-neural network modelling approach for sales forecasting, Int. J. Prod. Econ. 43 (1996) 175–192. [42] M.V.D. Voort, M. Dougherty, S. Watson, Combining kohonen maps with ARIMA time series models to forecast traffic flow, Transp. Res. C 4 (1996) 307–318. [43] R. Tsaih, Y. Hsu, C.C. Lai, Forecasting S&P 500 stock index futures with a hybrid AI system, Decis. Support Syst. 23 (1998) 161–174. [44] E.L. Lehmann, George Casella, Theory of Point Estimation, Springer-Verlag, NY, 1998. [45] P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-day Inc., San Francisco, CA, 1976. [46] E. Hajizadeh, A. Seifi, M.H. Fazel Zarandi, I.B. Turksen, A hybrid modelling approach for forecasting the volatility of S&P 500 index return, Expert Syst. Appl. 39 (2012) 431–436. [47] X. Jiang, A.H.K.S. Wah, Constructing and training feed-forward neural networks for pattern classification, Pattern Recognit. 36 (2003) 853–867. [48] B. Efron, Bootstrap methods: Another look at the Jacknife, Ann. Statist. 7 (1) (1979) 1–26. [49] B. Efron, R.J. Tibshirani, An Intoduction To the Bootstrap, Chapman & Hall, New York, London, 1993. [50] Z. Nurul Hila, Moving Centreline Exponentially Weighted Moving Average Based on Bootstrap Bootstrap Approach: Case Study of Sukuk Ijarah, (Ph.D. thesis), Universiti Malaysia Terengganu, Terengganu, Malaysia, 2016. [51] B. Efron, Estimating the error rate of a prediction rule: Improvement on cross validation, J. Am. Statist. Assoc 78 (1983) 316–331. [52] P. Hall, On bootstrap confidence intervals in nonparametric regression, Ann. Statist. 20 (2) (1992) 695–711. [53] M.R. Chernick, Bootstrap Methods: A Guide for Practitioners and Researchers, John Wiley & Sons, Inc., New Jersey, 2008. [54] R. Beran, Prepivoting to reduce level error of confidence sets, Biometrika 74 (3) (1987) 457–468. [55] M.A. Martin, On the Double Bootstrap. Technical Report No. 347 (Ms. 1-14), Stanford University, California, 1990. [56] P. Hall, M.A. Martin, On bootstrap resampling and iteration, Oxford J. 75 (4) (1998) 661–671. [57] J. Jeong, G.S. Maddala, Testing the rationality of survey data using the weighted double-bootstraped method of moments, Rev. Econ. Stat. 78 (2) (1996) 296–302. [58] F.T. Burbrink, A. Pyron, The taming of the skew: Estimating proper confidence intervals for divergence dates, Syst. Biol. 57 (2) (2008) 317–328. [59] D.M. Hillis, J.J. Bull, An empirical test of bootstrapping as a method for assesessing confidence in phylogenetic studies, Syst. Biol. 42 (1993) 182–192. [60] S.B. Hedges, The number of replications needed for accurate estimation of the bootstrap p-value in phylogenetic studies, Mol. Bio. Evol. 9 (1992) 366–369. [61] L. Muhamad Safiih, Z. Nurul Hila, R. Mohd Noor Afiq, A.R. Muhamad Na’eim, A. Mohd Tajuddin, Improvement of estimation based on small number of events per variable (EPV) using bootstrap logistics regression model, Malaysian J. Fundam. Appl. Sci. 13 (4) (2017) 693–704. [62] M.R. Chernick, Bootstrap Methods: A Guide for Practitioners and Researchers, John Wiley & Sons, Inc, New Jersey, 2008. [63] R. Beran, Prepivoting to reduce level error of confidence sets, Biometrika 74 (3) (1987) 457–468. [64] P. Hall, M.A. Martin, On bootstrap resampling and iteration, Oxford Journal 75 (4) (1998) 661–671. [65] Z. Nurul Hila, L. Muhamad Safiih, K. Nur Shazrahanim, Modelling movingcenterline exponentially weighted moving average (MCEMA) with bootstrap approach: Case study on sukuk musyarakah of Rantau Abang Capital berhad, Malaysia, Int. J. Appl. Bus. Econ. Res. 14 (2) (2016) 621–638. [66] Z. Nurul Hila, L. Muhamad Safiih, The performance of BB-MCEWMA model: Case study on sukuk Rantau Abang Capital Berhad Malaysia, Int. J. Appl. Bus. Econ. Res. 14 (2) (2016) 639–653. [67] L. Muhamad Safiih, Z. Nurul Hila, R. Mohd Noor Afiq, S. Hizir, Double bootstrap control chart for monitoring sukuk volatility at Bursa Malaysia, J. Teknol. 79 (6) (2017a) 149–157. [68] B. Chen, Y.R. Gel, N. Balakrishna, B. Abraham, Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes, J. Forecast. 30 (2011) 51–71 [69] L. Pascual, J. Romo, E. Ruiz, Bootstrap prediction for returns and volatility in GARCH models, Comput. Statist. Data Anal. 50 (2006) 2293–2312. [70] I.C. Nedelea, J.M. Fannin, Technical efficiency of critical access hospitals: An application of the two-stage approach with double bootstrap, Health Care Manage. Sci. 16 (2013) 27–36. [71] S. Dougles, Bootstrap confidence intervals in a switching regressionsmodel, Econ. Lett. 53 (1996) 7–15. [72] W.Y. Goh, C.P. Lim, K.K. Peh, Predicting drug dissolution profiles with an ensemble of boosted neural networks: A time series approach, IEEE Trans. Neural Netw. 14 (2) (2003) 459–463. [73] K.W. Hipel, A.I. McLeod, Time Series Modelling of Water Resources and Environmental Systems, Elsevier, Amsterdam, 1994. |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |