Time Series Forecasting Using a Hybrid RBF Neural Network and AR Model Based On Binomial Smoothing
ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.
[1] G. Peter Zhang, Time series forecasting using a hybrid ARIMA and neural
network model, Neurocomputing, vol. 50, pp. 159 - 175, 2003.
[2] A. Chaouachi, R.M. Kamel, R. Ichikawa, H. Hayashi, and K. Nagasaka,
Neural Network Ensemble-based Solar Power Generation Short-Term
Forecasting, International Journal of Information and Mathematical
Sciences, vol. 5, pp. 332-337, 2009.
[3] S. Makridakis, A. Anderson, R. Carbone, R. Fildes, M. Hibdon,
R. Lewandowski, J. Newton, E. Parzen, R. Winkler , The accuracy of
extrapolation (time series) methods: Results of a forecasting competition.
J. Forecasting, vol. 1, pp. 111-153, 1982.
[4] D.K. Wedding II, K.J. Cios, Time series forecasting by combining RBF
networks, certainty factors, and the Box-Jenkins model. Neurocomputing,
vol. 10, pp. 149-168, 1996.
[5] Areekul, P., Senjyu, T., Toyama, H., Yona, A., A Hybrid ARIMA and
Neural Network Model for Short-Term Price Forecasting in Deregulated
Market, IEEE Transactions on Power Systems , vol. 25, pp. 524-530,
2010.
[6] Durdu O¨ mer Faruk, A hybrid neural network and ARIMA model for
water quality time series prediction, Engineering Applications of Artificial
Intelligence, vol. 23, pp. 586-594, 2010.
[7] Mehdi Khashei, Seyed Reza Hejazi, Mehdi Bijari, A new hybrid artificial
neural networks and fuzzy regression model for time series forecasting,
Fuzzy Sets and Systems, vol. 159, pp. 769-786, 2008.
[8] C.J. Dong and Z.Y. Liu, Multi-layer neural network involving chaos
neurons and its application to traffic-flow prediction Journal of System
Simualtion, vol. 19, pp. 4450-4453, 2007.
[9] Y.L. Gu, C.F. Shao, and X.Y. Tan, Dynamic traffic volume forecast
method based on chaotic neural network, Traffic & Computer, vol. 25,
pp. 28-30, 2007.
[10] Ying Chen, Luh, P.B., Che Guan, Yige Zhao, Michel, L.D., Coolbeth,
M.A., Friedland, P.B., Rourke, S.J., Short-Term Load Forecasting:
Similar Day-Based Wavelet Neural Networks, IEEE Transactions on
Power Systems, vol. 25, pp. 322-330, 2010.
[11] X.L. ZHANG, and G.G. HE, Forecasting approach for short-term
traffic flow based on principal component analysis and combined neural
network, Systems Engineering - Theory & Practice, vol. 27, pp. 167-171,
2007.
[12] G.J. Song. C. Hu, K.Q. Xie, and R. Peng, Process neural network
modeling for real time short-term traffic flow prediction, Journal of Traffic
and Transportation Engineering, vol. 9, pp. 73-77, 2009.
[13] Zarita Zainuddin, Ong Pauline and Cemal Ardil, A Neural Network
Approach in Predicting the Blood Glucose Level for Diabetic Patients,
International Journal of Information and Mathematical Sciences, vol. 5,
pp. 72-79, 2009.
[14] C.J. Dong and Z. YLiu, A simulation study of artificial neural networks
for nonlinear time-series forecasting , Computers & Operations Research,
vol. 28, pp. 381-396, 2001.
[15] P. Marchand and L. Marmet, Binomial smoothing filter: A way to avoid
some pitfalls of least-squares polynomial smoothing, Review of Scientific
Instruments, vol. 54, pp. 1034 - 1041, 1983.
[16] Liu Hong, Cui Wenhua, Zhang Qingling, Nonlinear Combination Forecasting
Model and Application Based on Radial Basis Function Neural
Networks, IITA International Conference on Control, Automation and
Systems Engineering, Zhangjiajie, 2009, pp. 387-390.
[17] H. Akaike, A New Look at the Statistical Model Identification, IEEE
Trans.Automat.Control, vol. 19 pp. 716-723, 1974.
[18] C. Chatfield, Model uncertainty and forecast accuracy, J. Forecasting,
vol. 15 pp. 495-508, 1996.
[19] M.J. Campbell, A.M. Walker, A survey of statistical work on the
MacKenzie River series of annual Canadian lynx trappings for the years
1821-1934 and a new analysis, J. R. Statist. Soc. Ser. A , vol. 140 pp.
411-431, 1977.
[1] G. Peter Zhang, Time series forecasting using a hybrid ARIMA and neural
network model, Neurocomputing, vol. 50, pp. 159 - 175, 2003.
[2] A. Chaouachi, R.M. Kamel, R. Ichikawa, H. Hayashi, and K. Nagasaka,
Neural Network Ensemble-based Solar Power Generation Short-Term
Forecasting, International Journal of Information and Mathematical
Sciences, vol. 5, pp. 332-337, 2009.
[3] S. Makridakis, A. Anderson, R. Carbone, R. Fildes, M. Hibdon,
R. Lewandowski, J. Newton, E. Parzen, R. Winkler , The accuracy of
extrapolation (time series) methods: Results of a forecasting competition.
J. Forecasting, vol. 1, pp. 111-153, 1982.
[4] D.K. Wedding II, K.J. Cios, Time series forecasting by combining RBF
networks, certainty factors, and the Box-Jenkins model. Neurocomputing,
vol. 10, pp. 149-168, 1996.
[5] Areekul, P., Senjyu, T., Toyama, H., Yona, A., A Hybrid ARIMA and
Neural Network Model for Short-Term Price Forecasting in Deregulated
Market, IEEE Transactions on Power Systems , vol. 25, pp. 524-530,
2010.
[6] Durdu O¨ mer Faruk, A hybrid neural network and ARIMA model for
water quality time series prediction, Engineering Applications of Artificial
Intelligence, vol. 23, pp. 586-594, 2010.
[7] Mehdi Khashei, Seyed Reza Hejazi, Mehdi Bijari, A new hybrid artificial
neural networks and fuzzy regression model for time series forecasting,
Fuzzy Sets and Systems, vol. 159, pp. 769-786, 2008.
[8] C.J. Dong and Z.Y. Liu, Multi-layer neural network involving chaos
neurons and its application to traffic-flow prediction Journal of System
Simualtion, vol. 19, pp. 4450-4453, 2007.
[9] Y.L. Gu, C.F. Shao, and X.Y. Tan, Dynamic traffic volume forecast
method based on chaotic neural network, Traffic & Computer, vol. 25,
pp. 28-30, 2007.
[10] Ying Chen, Luh, P.B., Che Guan, Yige Zhao, Michel, L.D., Coolbeth,
M.A., Friedland, P.B., Rourke, S.J., Short-Term Load Forecasting:
Similar Day-Based Wavelet Neural Networks, IEEE Transactions on
Power Systems, vol. 25, pp. 322-330, 2010.
[11] X.L. ZHANG, and G.G. HE, Forecasting approach for short-term
traffic flow based on principal component analysis and combined neural
network, Systems Engineering - Theory & Practice, vol. 27, pp. 167-171,
2007.
[12] G.J. Song. C. Hu, K.Q. Xie, and R. Peng, Process neural network
modeling for real time short-term traffic flow prediction, Journal of Traffic
and Transportation Engineering, vol. 9, pp. 73-77, 2009.
[13] Zarita Zainuddin, Ong Pauline and Cemal Ardil, A Neural Network
Approach in Predicting the Blood Glucose Level for Diabetic Patients,
International Journal of Information and Mathematical Sciences, vol. 5,
pp. 72-79, 2009.
[14] C.J. Dong and Z. YLiu, A simulation study of artificial neural networks
for nonlinear time-series forecasting , Computers & Operations Research,
vol. 28, pp. 381-396, 2001.
[15] P. Marchand and L. Marmet, Binomial smoothing filter: A way to avoid
some pitfalls of least-squares polynomial smoothing, Review of Scientific
Instruments, vol. 54, pp. 1034 - 1041, 1983.
[16] Liu Hong, Cui Wenhua, Zhang Qingling, Nonlinear Combination Forecasting
Model and Application Based on Radial Basis Function Neural
Networks, IITA International Conference on Control, Automation and
Systems Engineering, Zhangjiajie, 2009, pp. 387-390.
[17] H. Akaike, A New Look at the Statistical Model Identification, IEEE
Trans.Automat.Control, vol. 19 pp. 716-723, 1974.
[18] C. Chatfield, Model uncertainty and forecast accuracy, J. Forecasting,
vol. 15 pp. 495-508, 1996.
[19] M.J. Campbell, A.M. Walker, A survey of statistical work on the
MacKenzie River series of annual Canadian lynx trappings for the years
1821-1934 and a new analysis, J. R. Statist. Soc. Ser. A , vol. 140 pp.
411-431, 1977.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:58423", author = "Fengxia Zheng and Shouming Zhong", title = "Time Series Forecasting Using a Hybrid RBF Neural Network and AR Model Based On Binomial Smoothing", abstract = "ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.
", keywords = "Binomial smoothing (BS), hybrid, Canadian Lynx data, forecasting accuracy.", volume = "5", number = "3", pages = "409-5", }
