Methods of Forecasting Based on Artificial Neural Networks

Arthur Stepchenko, Arkady Borisov

Abstract


This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems – models that fuse artificial neural network with wavelets and auto- regressive integrated moving average (ARIMA).

Keywords:
ARIMA ANN; forecasting; GRANN_ARIMA; WANN

Full Text:

 PDF

References


G. Zhang, B. E. Patuwo and M. Y. Hu, “Forecasting with artificial neural networks: the state of the art,” International Journal of Forecasting, vol. 14, no. 1, pp. 35–62, March 1998. [Online]. Available:

ResearchGate, http://www.researchgate.net/. [Accessed July 15, 2014]. http://dx.doi.org/10.1016/S0169-2070(97)00044-7

M. A. Shahin, M. B. Jaksa and H. R. Maier, “State of the art of artificial neural networks in geotechnical engineering,” EJGE Special Volume Bouquet 08. [Online]. Available: EJGE, http://www.ejge.com/. [Accessed July 18, 2014].

C. H. Aladag, E. Egrioglu and C. Kadilar, “Forecasting nonlinear time series with a hybrid methodology,” Applied Mathematics Letters, vol. 22, no. 9, pp. 1467–1470, September 2009. [Online]. Available: ScienceDirect, http://www.sciencedirect.com/. [Accessed July 20, 2014]. http://dx.doi.org/10.1016/j.aml.2009.02.006

A. Shabri and R. Samsudin, “Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model,” Mathematical Problems in Engineering, vol. 2014, article ID 201402, July 2014. [Online]. Available: Hindawi, http://www.hindawi.com/. [Accessed July 22, 2014]. http://dx.doi.org/10.1155/2014/201402

M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479–489, January 2010. [Online]. Available: Science Direct, http://www.sciencedirect.com/. [Accessed July 26, 2014]. http://dx.doi.org/10.1016/j.eswa.2009.05.044

R. Sallehudin, S. M. H. Shamsuddin, S. Z. M. Hashim and A. Abraham, “Hybrid Grey Relational Artificial Neural Network and Auto Regressive Integrated Moving Average Model for Forecasting Time-Series Data,” Applied Artificial Intelligence: An International Journal, vol. 23, no. 5, pp. 443–486, April 2009. [Online]. Available: ResearchGate, https://www.researchgate.net/. [Accessed July 30, 2014].

M. Adya and F. Collopy, “How effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation,” Journal of Forecasting, vol. 17, no. 5–6, pp. 481–495, Sep.–Nov. 1998. [Online]. Available: Weatherhead School of Management, http://weatherhead.case.edu/. [Acessed July 21, 2014]. http://dx.doi.org/10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q

S. T. A. Niaki and S. Hoseinzade, “Forecasting S&P index using artificial neural networks and design of experiments,” Journal of Industrial Engineering International, vol. 9, no. 1, Dec. 2013. [Online]. Available: Springer, http://link.springer.com. [Accessed July 24, 2014].

N. Q. Hung, M. S. Babel, S. Weesakul and N. K. Tripathi, “An artificial neural network model for rainfall forecasting in Bangkok, Thailand,” Hydrology and Earth System Sciences, vol. 13, no. 8, pp. 1413–1425, 2009. [Online]. Available: Hydrology and Earth System Sciences, http://www.hydrol-earth-syst-sci.net. [Accessed July 25, 2014]. http://dx.doi.org/10.5194/hess-13-1413-2009

I. Kasstra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing, vol. 10, no. 3, pp. 215–236, Apr. 1996. [Online]. Available: Geocities, http://www.geocities.ws/. [Accessed July 27, 2014]. http://dx.doi.org/10.1016/0925-2312(95)00039-9

G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, Jan. 2003. [Online]. Available: Geocities, http://www.geocities.ws/. [Accessed July 28, 2014]. http://dx.doi.org/10.1016/S0925-2312(01)00702-0

L. F. Ortega, “A Neuro-wavelet Method for the Forecasting of Financial Time Series,” Proceedings of the World Congress on Engineering and Computer Science, vol. I, San Franciso, USA, Oct. 24–26, 2012. [Online]. Available: International Association of Engineers, http://www.iaeng.org/. [Accessed July 29, 2014].


Refbacks

  • There are currently no refbacks.


Copyright (c) 2014 Arthur Stepchenko, Arkady Borisov

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.