Robust prediction in nearly periodic time series using motifs

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2014, Pages 2003-2010

  Chai, Woon Huei ^a   Guo, Hongliang ^a   Ho, Shen Shyang ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

AVAILABILITY; FORECASTING; NETWORK LAYERS; TIME SERIES;

AVAILABILITY PREDICTIONS; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; PERIODIC PROPERTIES; PREDICTION TASKS; ROBUST PREDICTIONS; SEQUENTIAL PATTERNS; SEQUENTIAL PREDICTION; TRANSITION PROBABILITIES;

MULTILAYER NEURAL NETWORKS;

EID: 84908472958     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889797     Document Type: Conference Paper

References (31)

1. Alspector, J., Gupta, B., & Allen, R. B. (1990, January). Performance of a stochastic learning microchip. In Artificial neural networks (pp. 66-78). IEEE Press.
2. Bloomfield, P. (2004). Fourier analysis of time series: an introduction. Wiley.com.
3. Bodyanskiy, Y., & Popov, S. (2006). Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research, 175(3), 1357-1366.
4. Broszkiewicz-Suwaj, E., Makagon, A., Weron, R., & WyÅĆomaÅɰska, A. (2004). On detecting and modeling periodic correlation in financial data. Physica A: Statistical Mechanics and its Applications, 336(1), 196-205.
5. Dolence, J. C., Gammie, C. F., Shiokawa, H., & Noble, S. C. (2012). Near-infrared and X-Ray Quasi-periodic Oscillations in Numerical Models of Sgr A∗. The Astrophysical Journal Letters, 746(1), L10.
6. Eiben, A. E., Aarts, E. H., & Van Hee, K. M. (1991). Global convergence of genetic algorithms: A Markov chain analysis. In Parallel problem solving from nature (pp. 3-12). Springer Berlin Heidelberg.
7. Furuya, M., Hamano, Y., & Naito, I. (1996). Quasi-periodic wind signal as a possible excitation of Chandler wobble. Journal of geophysical research, 101(B11), 25537-25.
8. Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of forecasting, 4(1), 1-28.
9. Gardner, W. A. (1994). Cyclostationarity in communications and signal processing. STATISTICAL SIGNAL PROCESSING INC YOUNTVILLE CA.
10. Giacomelli, G., Giudici, M., Balle, S., & Tredicce, J. R. (2000). Experimental evidence of coherence resonance in an optical system. Physical review letters, 84(15), 3298.
11. Gotoda, H., Ikawa, T., Maki, K., & Miyano, T. (2012). Short-term prediction of dynamical behavior of flame front instability induced by radiative heat loss. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(3), 033106-033106.
12. Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. Neural Networks, IEEE Transactions on, 5(6), 989-993.
13. Johnston, F. R., Boyland, J. E., Meadows, M., & Shale, E. (1999). Some properties of a simple moving average when applied to forecasting a time series. Journal of the Operational Research Society, 1267-1271.
14. Kagan, Y. Y., & Jackson, D. D. (1991). Seismic gap hypothesis: Ten years after. Journal of Geophysical Research: Solid Earth (1978âAş2012), 96(B13), 21419-21431.
15. Keeler, J. (1972). Piecewise-periodic analysis of almost-periodic sounds and musical transients. Audio and Electroacoustics, IEEE Transactions on, 20(5), s338-344.
16. Keeling, C. D., & Whorf, T. P. (2000). The 1, 800-year oceanic tidal cycle: A possible cause of rapid climate change. Proceedings of the National Academy of Sciences, 97(8), 3814-3819.
17. Kemeny, J. G. & Snell, J. L. (1976) FiniteMarkov Chains: Springer-Verlag, New York, 224 pp.
18. Li, Z., Wang, J., & Han, J. (2012, August). Mining event periodicity from incomplete observations. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 444-452). ACM.
19. Lonardi, J. L. E. K. S., & Patel, P. (2002). Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68).
20. Mann, S., & Haykin, S. (1991, June). The chirplet transform: A generalization of Gabor's logon transform. In Vision Interface (Vol. 91, pp. 205-212).
21. Mueen, A., Keogh, E. J., Zhu, Q., Cash, S., & Westover, M. B. (2009, April). Exact Discovery of Time Series Motifs. In SDM (pp. 473-484).
22. Osborn, D. R., & Smith, J. P. (1989). The performance of periodic autoregressive models in forecasting seasonal UK consumption. Journal of Business & Economic Statistics, 7(1), 117-127.
23. Rajagopalan, B., Lall, U., & Tarboton, D. G. (1996). Nonhomogeneous Markov model for daily precipitation. Journal of Hydrologic Engineering, 1(1), 33-40.
24. Rosenblatt, F. (1962). Principles of neurodynamics.
25. Sansone, A., & Garofalo, G. (2007). Asset price dynamics in a financial market with heterogeneous trading strategies and time delays. Physica A: Statistical Mechanics and its Applications, 382(1), 247-257.
26. Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 936). John Wiley & Sons.
27. Stamov, G. T., Alzabut, J. O., Atanasov, P., & Stamov, A. G. (2011). Almost periodic solutions for an impulsive delay model of price fluctuations in commodity markets. Nonlinear Analysis: Real World Applications, 12(6), 3170-3176.
28. Strang, G. (1989). Wavelets and dilation equations: A brief introduction. SIAM review, 31(4), 614-627.
29. Suzuki, J. (1995). A Markov chain analysis on simple genetic algorithms. Systems, Man and Cybernetics, IEEE Transactions on, 25(4), 655-659.
30. Tesauro, G. (1989). Neurogammon wins computer Olympiad. Neural Computation, 1(3), 321-323.
31. Winniford, M. (2003). Real Estate Investment Trusts and seasonal volatility: a periodic GARCH model. Duke University, Durham, NC. Spring.