Abstract
This research proposes estimation of mid-terms in a time series for improving the prediction performance of back propagation. In this research, the process of estimating mid-terms is called VTG (virtual term generation) and schemes for doing that are called VTG schemes. This research proposes three VTG schemes: mean method, 2nd Lagrange method, and 1st Taylor method. We adopt only back propagation as prediction model, since the goal of this research is to improve its prediction performance and back propagation is used most popular for regression among supervised neural networks. By implementing the VTG schemes as preprocessing of time series prediction, it will be observed that the prediction performance of back propagation is improved through experiments of Sect. 5.
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References
Abu-Mustafa YS (1993) A method for learning from hints. Adv Neural Inf Process Syst 5:73–80
Abu-Mustafa YS (1995) Financial application of learning from hints. Adv Neural Inf Process Syst 7:411–418
An G (1996) The effect of adding noise during back propagation training on generalization performance. Neural Computation 7:643–674
Box GEP, Jekins GM, Reinsel GC (1994) Time series analysis. Prentice Hall, Englewood Cliffs
Brownstone D (1996) Using percentage accuracy to measure neural network predictions in stock market movements. Neurocomputing 10(3):237–250
Cho S, Jang M, Chang S (1996) Virtual sample generation using a population of neural networks. Neural Proc Lett 5:83–89
Faires I, Douglas J (1993) Numerical analysis. PWS
Ghosn J, Bengio Y (1997) Multi-task learning for stock selection. Neural Inf Process Syst 9:947–952
Grandvalet Y, Canu S, Boucheron S (1997) Noise injection: theoretical prospects. Neural Comput 9(5):1093–1108
Jo T (1997) MLP training with noise optimized by genetic algorithm. In workshop of EUROGEN 97
Kohzhadi N, Boyd MS, Kermanshani B, Kaastra I (1996) A comparison of artificial neural network and time series models forecasting commercial commodities. Neurocomputing 10:169–181
Levin AU (1996) Stock selection via nonlinear multilayer factor models. Neural Inf Proc Syst 8:966–977
Malliaris M, Salchenberger L (1996) Using neural networks to forecast the S & P 100 implied volatility. Neurocomputing 10(2):183–195
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: exploration in the microstructure of cognition. MIT Press, Cambridge
Master T (1995) Neural, novel & hybrid algorithms for time series prediction. Wiley, New York
Wan EA (1994) Time Series prediction by using a connectionist network with internal delay lines. In: Weigend AS, Gershenfeld NA (eds) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, pp 195–217
Weigend AS, Huberman BA, Rumelhart DE (1990) Predicting the future: a connectionist approach. International Journal of Neural Systems 193–209
Weigend AS, Rumelhart DE (1991) Generalization through minimal networks with application to forecasting. The proceedings of 23rd symposium interface. pp 362–370
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Jo, T. The effect of mid-term estimation on back propagation for time series prediction. Neural Comput & Applic 19, 1237–1250 (2010). https://doi.org/10.1007/s00521-010-0352-1
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DOI: https://doi.org/10.1007/s00521-010-0352-1