We propose a forecasting model based on the combined efficiency of the artificial neural network and wavelet transform in modelling time series data. The data used were decomposed into continuous wavelet signals on a scale of 10. Each of the decomposed series was subjected to correlation test with the original data. We compare the new model's performance with the conventional time series regression model (TSRM) and the wavelet neural network (WNN) forecasting model. The WNN model performed better than the TSRM. The analysis also showed that except in extremely rare cases, all the wavelet series performed optimally compared to the original data.
Adamowski, J. and Chan, H.F. (2011) A Wavelet Neural Network Conjunction Model for Groundwater Level Forecasting. Journal of Hydrology, 407: 28-40.
Armstrong, S. and Adyaa, M. (2000). An application of rule-based forecasting to a situation lacking domain knowledge. International Journal of Forecasting, 16: 477-484.
Aussem, J.C.A and Murtagh, F. (1998). Wavelet-based feature extraction and decomposition strategies for financial forecasting. Journal of Computational Intelligence in Finance, 6: 5-12.
Chen, B.Y.Y. and Abraham, A. (2006). Optimal design of hierarchical wavelet networks for time series forecasting. Proceedings of ESANN 2006: 155-160.
Debes, K., Koenig, A. and Gross, H. (2005). Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial. A Supplementary Material for urn: nbn:de:0009-3-1515, Department of Neuroinformatic and Cognitive Robotics, Technical University Ilmenau, Germany. Retrieved from http//: www.brains-minds-media.org.
Dorofki, M., Elshafie,
A.H., Jaafar, O., Karim, O.A. and Mastura, S. (2012). Comparison of Artificial
Neural Network Transfer Functions abilities to simulate Extreme Runoff Data.
Proceedings of the 2012 International Conference on Environment, Energy and
Biotechnology, Singapore 2012, IPCBEE, 33: 39-44.
Dunis, C.L. and Williams,
M. (2002). Modelling and trading the EUR/USD exchange rate: Do neural network
models perform better? Derivatives Use, Trading & Regulation,8: 211-239.
Fay, D. and Ringwood,
J.V. (2007). A wavelet transfer model for time series forecasting.
International Journal of Bifurcation and Chaos, 17: 3691-3696.
Funahashi, K. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2: 183-192.Gradojevic, N. and Yang, J. (2000). The application of artificial neural networks to exchange rate forecasting: The role of market microstructure variables. Bank of Canada Working Paper, 23.
Hornik, K. (1993). Some
new results on neural network approximation. Neural Networks, 6: 1069-1072.
Hornik, K., Stinchcombe,
M. and White, H. (1989). Multilayer feedforward networks are universal
approximators. Neural Networks, 2: 359-366.
Irie, B. and Miyake, S. (1988). Capabilities of three-layered perceptrons. Proceedings of the First Forum on Application of Neural Networks to Power Systems, 41-45.
Kaashoek, J.F. and van Dijk, H.K. (2002). Neural network pruning applied to real exchange rate analysis. Journal of Forecasting, 21: 559-577.
Kilby, J. and Prasad, K.
(2013). Continuous wavelet analysis and classification of surface
electromyography signals. International Journal of Computer and Electrical
Engineering, 5(1): 30-35
Krishna, B., Satyaji Rao,
Y.R. and Nayak, P.C. (2011). Time series modelling of river flow using wavelet
neural networks. Journal of Water Resource and Protection, 3:50-59.
Lai, S.W.K.K., Yu, L. and Zhou, C. (2006). Neural-network-based meta-modelling for financial time series forecasting. Proceedings of the 2006 Joint Conference on Information Sciences.
Mishra, S., Saravanan, C. and Dwivedi, D.K. (2015). Study of time series data mining for the real time hydrological forecasting - a review. International Journal of Computer Applications, 117(23): 6-17.
Mitra, A. and Mitra, S. (2006). Modelling exchange rates using wavelet decomposed genetic neural networks. Statistical Methodology, 3(2): 103-124.
Pandhiani, S.M and Shabri, A.B. (2013). Time Series Forecasting using Wavelet-Least Squares Support Vector Machines and Walet Regression Models for Monthly Stream Flow Data. Open Journal of Statistics, 3: 193-194.
Qiu, C.R. and Rong, Z. (2008). A case-based reasoning system for individual demand forecasting. 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM '08: 1-6.
Ramsey, J.B. (2002). Wavelets in economics and finance: Past and future. Working Paper No. S-MF-02-02, C.V. Starr Center for Applied Economics, New York University.
Ripley, B.D. (1993). Statistical aspects of neural networks.In:Barndo-Nielsen, O. E., Jensen, J. L. (Eds), Networks and Chaos-Statistical and Probabilistic Aspects, Chapman & Hall, London, 40-123.
Sallehuddin, S.Z.M.H.R, Shamsuddin, S.M.H. and Abraham, A. (2007). Forecasting time series data using hybrid grey relational artificial neural network and auto-regressive integrated moving average model. Neural Network World, 6: 573-605.
Satyaji Rao, Y.R., Krishna, B. and Venkatesh, B. (2014). Wavelet based neural networks for daily stream
ow forecasting. International Journal of Emerging Technology and Advanced Engineering, 4(1): 307-317.
Song, D., Zhang, Y.,
Shan, X., Cui, J. and Wu, H. (2017). Over-learning phenomenon of wavelet neural
networks in remote sensing image classification with di
erent entropy error functions. Entropy, 19(101): 1-19.
Tan, C. (2009). Financial time series forecasting using improved wavelet neural network. Unpublished Master's Thesis.
Tenti, P. (1996). Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial Intelligence, 10: 567-581.
Udomboso, C.G. (2014). On the level of precision of an alternative heterogeneous model of the statistical neural network. Unpublished Ph.D. Thesis, University of Ibadan, Ibadan.
Veitch, D. (2005). Wavelet neural networks and their applications in the study of dynamical systems. MSc Dissertation in Data Analysis, Networks and Nonlinear Dynamics, Department of Mathematics, University of York, UK.
Wen, X., Zhang, H. and Wang, F. (2009). A wavelet neural network for SAR image segmentation. Sensors, 9: 7509-7515; doi:10.3390/s90907509.
White, H. (1989). Learning in artificial neural networks: a statistical perspective.Neural Computation, 1: 425-464.
Wu, B. (1995). Model-free forecasting for nonlinear time series with application to exchange rates. Computational Statistics & Data Analysis, 19: 433-459.
Yonaba, H., Anctil, F. and Fortin, V. (2010). Comparing sigmoid transfer functions for neural network multistep ahead stream
ow forecasting. Journal of Hydrological Engineering, ASCE/APRIL 2010: 275-283. Retrieved from http://www.ascelibrary.org.
Yousefi, I.W.S. and Reinarz, D. (2005). Wavelet-based prediction of oil prices. Chaos, Solitons and Fractals, 25: 265-275.
Zhang B.L. and Dong Z.Y. (2001). An adaptive neural-wavelet model for short term load
forecasting. Electric Power Systems Research, 59: 121-129.
Zhang B.L. and Coggins R. (2001). Multi-resolution forecasting for futures trading using wavelet decompositions. IEEE Transactions on Neural Networks, 12(4): 765-775.
Zhang G.P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-175.
Zhang G., Patuwo B.E., and Hu M.Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14: 35-62.
SHARE WITH OTHERS