Prediction of Financial Time-Series Signals Using á Trous Wavelet Transform

Abstract:

This paper proposes a financial time-series prediction method consisting of á Trous wavelet transform and polynomial regression. The main purpose of employing á Trous wavelet transform is to decompose financial time-series signals into different resolutions where only relevant signal components are used for prediction. Also, á Trous wavelet transform is used to avoid the edge problem where only the past and present components of the time-series signal are taken into account. The decomposed time-series signals are then fed into the polynomial regression part to obtain predicted time-series signals. Using real-world data, performance evaluation is conducted based on total benefit and profit/loss where it is shown that á Trous wavelet transform contributes to a significant performance improvement.

• Periodical: Applied Mechanics and Materials (Volume 781)
• Pages: 523-526
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.781.523
• Online since: August 2015
• Authors: Wassanun Sangjun*, Supawat Supakwong, Suttipong Thajchayapong
• Keywords: á Trous Wavelet Transform, Time-Series Prediction, Time-Series Signal Processing

References

[1] Peter J. Brockwell and Richard A. Davis, Introduction, in Introduction to Time Series and Forecasting, 2nd ed.: Springer, New York, 2002, ch. 1, pp.1-44.

[2] D. Lee Fugal, Preview of Wavelets, Wavelet Filters and Wavelet Transforms, in Conceptual Wavelets In Digital Signal Processing.: Space & Signals Technical Pub., 2009, ch. 1, pp.1-30.

[3] Aiping Yang et al., Construction of Wavelet Transform Matrices with Symmetric Boundary-Extension, on Signal Processing, 2006 8th International Conference, vol. 2, no., pp., 16-20 (2006).

DOI: 10.1109/icosp.2006.345712

[4] Miha Boltežar and Janko Slavič, Enhancements to the continuous wavelet transform for damping identifications on short signals, on Mechanical Systems and Signal Processing, vol. 18, issue 5, September 2004, Pages 1065-1076.

DOI: 10.1016/j.ymssp.2004.01.004

[5] Renaud O. et al., Wavelet-Based Combined Signal Filtering and Prediction, in Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 35, no. 6, p.1241, 1251, Dec. (2005).

DOI: 10.1109/tsmcb.2005.850182

[6] Zhang, Bai-Ling et al., Multiresolution forecasting for futures trading using wavelet decompositions, on Neural Networks, IEEE Transactions, vol. 12, no. 4, p.765, 775, Jul (2001).

DOI: 10.1109/72.935090

[7] Perry J. Kaufman, Regression Analysis, in Trading Systems and Methods, +Website, 5th ed.: Wiley, USA, Jan. 2013, ch. 6, pp.235-278.