The Application of Qubit Neural Networks for Time Series Forecasting with Automatic Phase Adjustment Mechanism
Resumo
Quantum computation, quantum information and artificial intelligence have all contributed for the new non-standard learning scheme named Qubit Neural Network (QNN). In this paper, a QNN based on the qubit neuron model is used for real world time series forecasting problem, where one chaotic series and one stock market series were predicted. Experimental results show evidences that the simulated system is able to preserve the relative phase information of neurons quantum states and thus, automatically adjust the forecast’s time shift.Referências
Deutsch, D. (1985). Quantum theory, the Church-Turing principle and the universal quantum computer. Proceedings of the Royal Society of London, pages A400:97-117.
Feynman, R. (1982) Simulating physics with computers. In: International Journal of Theoretical Physics, pages 21:67-488.
Ferreira, T. A. E., Vasconcelos, G. C. and Adeodato, P. J. L. (2004). A hybrid intelligent system approach for improving the prediction of real world time series. IEEE In Proceedings of the Congress on Evolutionary Computation, pages 736-743. IEEE.
Ferreira, T. A. E., Vasconcelos, G. C. and Adeodato, P. J. L. (2005). A new hybrid approach for enhanced time series prediction. In Anais do XXV Congresso da Sociedade Brasileira de Computação, pages 831-840. SBC.
Ferreira, T. A. E. (2006). Uma nova metodologia híbrida inteligente para a previsão de séries temporais. Thesis (Ph.D.), Centro de Informática UFPE.
Fuller, W. A. (1976), Introduction to statistical time series, John Wiley & Sons, 2 nd edition.
Kouda, N. Matsui, N. and Nishimura, H. (2004). A multilayered feedforward network based on qubit neuron model. In Systems and Computers in Japan, pages 35(13):4351.
Kouda, N., Matsui, N., Nishimura, H. and Peper, F. (2005). An examination of qubit neural network in controlling an inverted pendulum. In Neural Processing Letters, pages 22(3):277-290, Springer Netherlands.
Lo, A. W. and MacKinlay, A. C. (2002). A non-random walk down Wall Street, Princeton University Press, 6 th edition.
Malkiel, B. G. (1973). A random walk down Wall Street, W. W. Norton & Company Inc., 6 th edition.
Matsui, N, Takai, M. and Nishimura, H. (2000). A network model based on qubit-like neuron corresponding to quantum circuit. In The Institute of Electronics Information and Communications in Japan (Part III: Fundamental Electronic Science), pages 83(10):67-73.
Mitrpanont, J. L. and Srisuphab, A. (2002). The realization of quantum complex-valued backpropagation neural network for pattern recognition problem. In Neural Information Processing, 2002. ICONIP’02. Proceedings of the 9 th International Conference on, pages 1:462-466.
Nielsen, M. A. and Chuang, I. L. (2005). Computação quântica e informação quântica, Bookman, 1ª edição.
Nitta, T. (1994). Structure of learning in the complex numbered back-propagation network. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence, 1994 IEEE International Conference on, pages 1:269-274.
Percival, D. B. and Walden, A. T. (1998). Spectral analysis for physical applications – multipaper and conventional univariate techniques, Cambridge University Press.
Prechelt, L. (1994). Proben1: a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94. Fakultat fur Informatik, Karlsruhe.
Prechelt, L. (1998) Automatic early stopping using cross-validation: quantifying the criteria. In Neural Networks, pages 11(4):761-767.
Shor, P. W. (1994) Algorithms for quantum computation: discrete logarithms and factoring. In Foundations of Computer Science, 1994 Proceedings, 35th Annual Symposium on, (ed. S. Goldwasser) p. 124-134. IEEE Computer Society Press.
Sitte, R. and Sitte, J. (2002) Neural networks approach to the random walk dilemma of financial time series. Applied Intelligence, 16(3):163-171.
Feynman, R. (1982) Simulating physics with computers. In: International Journal of Theoretical Physics, pages 21:67-488.
Ferreira, T. A. E., Vasconcelos, G. C. and Adeodato, P. J. L. (2004). A hybrid intelligent system approach for improving the prediction of real world time series. IEEE In Proceedings of the Congress on Evolutionary Computation, pages 736-743. IEEE.
Ferreira, T. A. E., Vasconcelos, G. C. and Adeodato, P. J. L. (2005). A new hybrid approach for enhanced time series prediction. In Anais do XXV Congresso da Sociedade Brasileira de Computação, pages 831-840. SBC.
Ferreira, T. A. E. (2006). Uma nova metodologia híbrida inteligente para a previsão de séries temporais. Thesis (Ph.D.), Centro de Informática UFPE.
Fuller, W. A. (1976), Introduction to statistical time series, John Wiley & Sons, 2 nd edition.
Kouda, N. Matsui, N. and Nishimura, H. (2004). A multilayered feedforward network based on qubit neuron model. In Systems and Computers in Japan, pages 35(13):4351.
Kouda, N., Matsui, N., Nishimura, H. and Peper, F. (2005). An examination of qubit neural network in controlling an inverted pendulum. In Neural Processing Letters, pages 22(3):277-290, Springer Netherlands.
Lo, A. W. and MacKinlay, A. C. (2002). A non-random walk down Wall Street, Princeton University Press, 6 th edition.
Malkiel, B. G. (1973). A random walk down Wall Street, W. W. Norton & Company Inc., 6 th edition.
Matsui, N, Takai, M. and Nishimura, H. (2000). A network model based on qubit-like neuron corresponding to quantum circuit. In The Institute of Electronics Information and Communications in Japan (Part III: Fundamental Electronic Science), pages 83(10):67-73.
Mitrpanont, J. L. and Srisuphab, A. (2002). The realization of quantum complex-valued backpropagation neural network for pattern recognition problem. In Neural Information Processing, 2002. ICONIP’02. Proceedings of the 9 th International Conference on, pages 1:462-466.
Nielsen, M. A. and Chuang, I. L. (2005). Computação quântica e informação quântica, Bookman, 1ª edição.
Nitta, T. (1994). Structure of learning in the complex numbered back-propagation network. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence, 1994 IEEE International Conference on, pages 1:269-274.
Percival, D. B. and Walden, A. T. (1998). Spectral analysis for physical applications – multipaper and conventional univariate techniques, Cambridge University Press.
Prechelt, L. (1994). Proben1: a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94. Fakultat fur Informatik, Karlsruhe.
Prechelt, L. (1998) Automatic early stopping using cross-validation: quantifying the criteria. In Neural Networks, pages 11(4):761-767.
Shor, P. W. (1994) Algorithms for quantum computation: discrete logarithms and factoring. In Foundations of Computer Science, 1994 Proceedings, 35th Annual Symposium on, (ed. S. Goldwasser) p. 124-134. IEEE Computer Society Press.
Sitte, R. and Sitte, J. (2002) Neural networks approach to the random walk dilemma of financial time series. Applied Intelligence, 16(3):163-171.
Publicado
30/06/2007
Como Citar
AZEVEDO, Carlos R. B.; FERREIRA, Tiago. A. E..
The Application of Qubit Neural Networks for Time Series Forecasting with Automatic Phase Adjustment Mechanism. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 1112-1121.
ISSN 2763-9061.
