Adaptive neural network model for time-series forecasting. (English) Zbl 1208.62151

Summary: A novel adaptive neural network \((ADNN)\) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust the forecasting results by the relative error and make them more accurate. The proposed \(ADNN\) method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the autoregression \((AR)\), artificial neural network \((ANN)\), and adaptive \(k\)-nearest neighbors \((AKN)\) models. The \(ADNN\) model is proved to benefit from the merits of the \(ANN\) and the \(AKN\) through its’ novel structure with high robustness particularly for both chaotic and real time-series predictions.


62M20 Inference from stochastic processes and prediction
62M45 Neural nets and related approaches to inference from stochastic processes
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
Full Text: DOI


[1] Adya, M.; Collopy, F., How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of forecasting, 17, 481-495, (1998)
[2] Barbounis, T.G.; Teocharis, J.B., Locally recurrent neural networks for wind speed prediction using spatial correlation, Information science, 177, 5775-5797, (2007)
[3] Bartlett, P.L., For valid generalization, the size of the weights is more important than the size of the network, (), 134-140
[4] Bodyanskiy, Y.; Popov, S., Neural network approach to forecasting of quasiperiodic financial time series, European journal of operational research, 175, 1357-1366, (2006) · Zbl 1142.91715
[5] Brooks, C., Introductory econometrics for finance, (2002), Cambridge University Press Cambridge, UK, p. 289 · Zbl 1015.91001
[6] Casdagli, M., Nonlinear prediction of chaotic time series, Physics D, 35, 335-356,, (1989) · Zbl 0671.62099
[7] Celik, A.E.; Karatepe, Y., Evaluating and forecasting banking crises through neural network models: an application for turkish banking sector, Expert systems with applications, 33, 809-815, (2007)
[8] Chen, S.M.; Hwang, J.R., Temperature prediction using fuzzy time series, IEEE transactions on systems, man and cybernetics part B, 30, 263-275, (2000)
[9] Cybenko, G., Approximation by superpositions of a sigmoidal function, Mathematics of control, signals, and systems, 2, 303-314, (1989) · Zbl 0679.94019
[10] Freitas, P.S.A.; Rodrigues, A.J.L., Model combination in neural-based forecasting, European journal of operational research, 173, 801-814, (2006) · Zbl 1120.90342
[11] Funahashi, K., On the approximate realization of continuous mappings by neural networks, Neural networks, 2, 183-192, (1989)
[12] Geman, S.; Bienenstock, E.; Doursat, R., Neural networks and the bias/variance dilemma, Neural computation, 4, 1-58, (1992)
[13] Hansen, J.V.; McDonald, J.B.; Nelson, R.D., Time series prediction with genetic-algorithm designed neural networks: an empirical comparison with modern statistical models, Computational intelligence, 15, 171-184, (2002)
[14] Huarng, K.; Yu, T.H., Ratio-based lengths of intervals to improve fuzzy time series forecasting, IEEE transactions on systems, man and cybernetics part B, 36, 328-340, (2006)
[15] Kim, D.; Kim, C., Forecasting time series with genetic fuzzy predictor ensemble, IEEE transactions on fuzzy systems, 5, 523-535, (1997)
[16] Kulesh, M.; Holschneider, M.; Kurennaya, K., Adaptive metrics in the nearest neighbours method, Physics D, 237, 283-291, (2008) · Zbl 1141.37033
[17] Liu, M.C.; Kuo, W.; Sastri, T., An exploratory study of a neural network approach for reliability data analysis, Quality and reliability engineering international, 11, 107-112, (1995)
[18] Makridakis, S., Forecasting: its role and value for planning and strategy, International journal of forecasting, 12, 513-537, (1996)
[19] Makridakis, S.; Wheelwright, S.C.; Hyndman, R.J., Forecasting-methods and applications, (1998), Wiley New York, pp. 42-50
[20] Masters, T., Advanced algorithms for neural networks: A C++ sourcebook, (1995), Wiley New York
[21] McDonnell, J.R.; Waagen, D., Evolving recurrent perceptrons for time series modeling, IEEE transactions on neural networks, 5, 24-38, (1994)
[22] Moody, J.E., The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems, Neural information processing systems, 4, 847-854, (1992)
[23] Murray, D.B., Forecasting a chaotic time series using an improved metric for embedding space, Physics D, 68, 318-325, (1993) · Zbl 0788.58037
[24] Press, W.H.; Teukolsky, S.A.; Vetterling, W.T.; Flannery, B.P., Numerical recipe in C: the art of scientific computing, (1992), Cambridge University Press · Zbl 0845.65001
[25] Sahoo, G.B.; Ray, C., Flow forecasting for a hawaii stream using rating curves and neural networks, Journal of hydrology, 317, 63-80, (2006)
[26] Singh, P.; Deo, M.C., Suitability of different neural networks in daily flow forecasting, Applied soft computing, 7, 968-978, (2007)
[27] Taylor, J.W.; Buizza, R., Neural network load forecasting with weather ensemble predictions, IEEE transactions on power systems, 17, 59, (2002)
[28] Wang, T.; Chien, S., Forecasting innovation performance via neural networks – a case of taiwanese manufacturing industry, Technovation, 26, 635-643, (2006)
[29] Wong, B.K.; Vincent, S.; Jolie, L., A bibliography of neural network business applications research: 1994-1998, Operations research and computers, 27, 1045-1076, (2000) · Zbl 0961.90001
[30] Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 159-175, (2003) · Zbl 1006.68828
[31] Zhang, P.; Qi, G.M., Neural network forecasting for seasonal and trend time series, European journal of operational research, 160, 501-514, (2005) · Zbl 1066.62094
[32] Zhang, G.; Eddy, P.B.; Hu, M.Y., Forecasting with artificial neural networks: the state of the art, International journal of forecasting, 14, 35-62, (1998)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.