Modelling and Analysis on Noisy Financial Time Series

Jinsong Leng

Bradford Street, Mount Lawley, Perth, Australia..

DOI: 10.4236/jcc.2014.22012                

Abstract

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular problem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively.

Keywords

Financial Time Series; Filtering and Denoising; Autoregression; Modelling and Prediction

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	G. U. Yule, “On a Method of Investigating Periodicities in Disturbed Series,” Philosophical Transactions of the Royal Society of London, Vol. 226, 1927, pp. 267-298. http://dx.doi.org/10.1098/rsta.1927.0007
[2]	T. C. Fu, “A Review on Time Series Data Mining,” Engineering Applications of Artificial Intelligence, Vol. 24, 2011, pp. 164-181. http://dx.doi.org/10.1016/j.engappai.2010.09.007
[3]	R. S. Tsay, “Analysis of Financial Time Series,” John Wiley & Sons, Inc., New York, 2010.
[4]	D. B. Percival and A. T. Walden, “Wavelet Methods for Time Series Analysis,” Cambridge University Press, Cambridge, 2000. http://dx.doi.org/10.1017/CBO9780511841040
[5]	F. Gustafsson, “Determining the Initial States in Forward-Backward Filtering,” IEEE Transactions on Signal Processing, Vol. 44, No. 4, 1996, pp. 988-992. http://dx.doi.org/10.1109/78.492552
[6]	I. Daubechies, “Ten Lectures on Wavelets,” Society for Industrial and Applied Mathematics, Philadelphia, 1992. http://dx.doi.org/10.1137/1.9781611970104
[7]	C. K. Chui, “An Introduction to Wavelets,” Academic Press, 1992.
[8]	S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, 1989, pp. 674-693. http://dx.doi.org/10.1109/34.192463
[9]	S. M. Pincus, “Approximate Entropy as a Measure of System Complexity,” National Academy of Sciences of the United States of America, Vol. 88, No. 6, 1991, pp. 2297-2301. http://dx.doi.org/10.1073/pnas.88.6.2297
[10]	S. M. Pincus, I. M. Gladstone and R. A. Ehrenkranz, “A Regularity Statistic for Medical Data Analysis,” Journal of Clinical Monitoring and Computing, Vol. 7, No. 4, 1991, pp.335-345.
[11]	S. M. Pincus and E. K Kalman, “Irregularity, Volatility, Risk, and Financial Market Time Series,” Proceedings of the National Academy of Sciences, Vol. 101, No. 38, 2004, pp. 13709-13714. http://dx.doi.org/10.1073/pnas.0405168101