© The Institution of Engineering and Technology
Chaotic multi-dimensional time series (MDTS) exist in some fields such as stock markets and life sciences. To effectively extract the desired information from the measured MDTS, it is important to preprocess data to reduce noise. On the basis of dictionary learning, a method to remove noise is proposed, and the proposed approach is shown to be very effective in the case of MDTS. An MDTS is first considered as a whole, namely an image, and then the method is applied on it. Compared with traditional methods, the proposed approach can utilise the information among the different dimensional time series to improve noise reduction. Using the Lorenz data superimposed by the Gaussian noise as an example, the simulation results have validated the mathematical framework and the performance.
References
-
-
1)
-
4. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: ‘Online dictionary learning for sparse coding’. Proc. of 26th Annual Int. Conf. on Machine Learning – ICML'09, Montreal, Canada, June 2009, pp. 1–8, .
-
2)
-
S.G. Mallat ,
Z. Zhang
.
Matching pursuits with time-frequency dictionaries.
IEEE Trans. Signal Process.
,
12 ,
3397 -
3415
-
3)
-
1. Han, M., Liu, Y., Xi, J., Guo, W.: ‘Noise smoothing for nonlinear time series using wavelet soft threshold’, IEEE Signal Process. Lett., 2007, 14, (10), pp. 62–65 (doi: 10.1109/LSP.2006.881518).
-
4)
-
2. Gao, J., Sultan, H., Hu, J., Tung, W.: ‘Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison’, IEEE Signal Process. Lett., 2010, 17, (3), pp. 237–240 (doi: 10.1109/LSP.2009.2037773).
-
5)
-
B. Olshausen ,
D. Field
.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
Nature
,
6583 ,
607 -
609
-
6)
-
3. Kostelich, E., Schreiber, T.: ‘Noise reduction in chaotic time-series data: a survey of common methods’, Phys. Rev. E, 1993, 48, (3), pp. 1752–1763 (doi: 10.1103/PhysRevE.48.1752).
-
7)
-
D.L. Donoho
.
’ Denoising by soft-thresholding.
IEEE Trans. Inf. Theory.
,
3 ,
613 -
627
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2014.1757
Related content
content/journals/10.1049/el.2014.1757
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Noise reduction in chaotic multi-dimensional time series using dictionary learning