Abstract
In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of training. Rather than using the last n lags for prediction, the input to the network is determined by the output of the PMRS (Pattern Modelling and Recognition System). PMRS matches current patterns in the time-series with historic data and generates input for the neural network that consists of both current and historic information. The results of the hybrid model are compared with those of neural networks and PMRS on their own. In general, there is no outright winner on all performance measures, however, the hybrid model is a better choice for certain types of data, or on certain error measures.
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References
Armstrong S. and Collopy F. Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting 1992; 8:69–80.
Azoff M. E. Neural Network Time Series Forecasting of Financial Markets. John Wiley, 1994.
Gately E. Neural Networks for Financial Forecasting. John Wiley, 1996.
Peters E. Fractal Market Hypothesis: Applying Chaos Theory to Investment and Economics. John Wiley, 1994.
Refenes A. N., Burgess N. and Bentz Y. Neural networks in financial engineering: A study in methodology. IEEE Transactions on Neural Networks 1997; 8:6:1222–1267.
Singh S. A long memory pattern modelling and recognition system for financial time-series forecasting. Pattern Analysis and Applications 1999; 2:3:264–273.
Singh S. and Fieldsend J. E. Financial Time Series Forecasts using Fuzzy and Long Memory Pattern Recognition Systems. IEEE International Conference on Computational Intelligence for Financial Engineering, New York, (26–28 March, 2000). IEEE Press, 2000.
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Singh, S., Fieldsend, J. (2001). Pattern Matching and Neural Networks based Hybrid Forecasting System. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_8
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DOI: https://doi.org/10.1007/3-540-44732-6_8
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