Abstract
Repetitive, but not strictly periodic, trends in the temporal data can present a challenge to the analysis of short-term patterns. Military examples of such time-series include violence data or vessel detections. Empirical mode decomposition (EMD) has been used across a variety of different fields such as biology and plasma physics to deal with non-stationarities in the data. This methodology enables separation of different modes intrinsic to the data and it does not require a priori assumptions about time dependence of various data sub-components, such as periodicity of variations. We show the application of this methodology to two distinct types of data. The Afghanistan violence data provided an example of a sparse, limited dataset. With EMD we were able to identify a multi-year cycle, without the skewed trend in the vicinity of turning points. In contrast, ship detection data for the Canadian West coast provide an example of a large data set. Unfortunately, the analysis of the summary detection data led to the presence of noise limiting our ability to identify specific patterns in the data. The analysis could be improved by geographically dividing the data into a number of small areas and conducting separate analysis for each area. Despite this, the EMD demonstrated its usefulness and applicability, enhancing the analysis of these two datasets compared to more conventional approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wiskott, L., Sejnowski, T.J.: Slow feature analysis: unsupervised learning of invariances. Neural Comput. 14(4), 715–770 (2002)
Gons, E., Schroden, J., McAlinden, R., Gaul, M., VanPoppel, B.: Challenges of measuring progress in Afghanistan using violence trends: the effects of aggregation, military operations, seasonality, weather, and other causal factors. Defence Secur. Anal. 28(2), 100–113 (2012)
Dobias, P., Horn, S., Liu, M.-J., Eisler, C., Sprague, K.: Use of fractal-based approaches in the assessment of the Canadian recognized maritime picture. In: Proceedings from 32nd International Symposium on Military Operational Research, London, UK (2015)
Dobias, P., Wanliss, J.A.: Empirical mode decomposition applied to Afghanistan violence data: comparison with multiplicative seasonal decomposition. J. Battlefield Technol. 17(2), 17–22 (2014)
Dobias, P., Eles, P., Schroden, J., Wanliss, J.: Quantitative analysis in support of military intelligence: predictive analysis and risk assessment in current security environment. In: Proceedings from the 28th ISMOR, Hampshire, UK (2011)
Wanliss, J.A., Dobias, P.: Dealing with non-stationarities in violence data using empirical mode decomposition. J. Battlefield Technol. 16(2), 2–5 (2013)
Radtke, H.D., Davis, S.W.: Description of the US West Coast commercial fishing fleet and sea food processors, Report for Pacific States Marine Fisheries Commission (2000). www.psmfc.org/efin/docs/fleetreport.pdf. Accessed 10 April 2018
Binder, T.R., Cooke, S.J., Hinch, S.G.: The biology of fish migration. In: Farrell, A.P. (ed.) Encyclopedia of Fish Physiology: From Genome to Environment, vol. 3, pp. 1921–1927. Academic Press, San Diego (2011)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, S.H., Zheng, Q., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A-Math. Phys. 454, 903–993 (1998). https://doi.org/10.1098/rspa.1998.0193
Yu, Z.-G., Anh, V., Wang, Y., Mao, D., Wanliss, J.: Modeling and simulation of the horizontal component of the geomagnetic field by fractional stochastic differential equations in conjunction with empirical mode decomposition. J. Geophys. Res. 115(A10219) (2010). https://doi.org/10.1029/2009ja015206
Anh, V., Yu, Z.-G., Wanliss, J.A.: Analysis of global geomagnetic variability. Nonlin. Process. Geophys. 14(6), 701–708 (2007). https://doi.org/10.5194/npg-14-701-2007
Wanliss, J.A., Weygand, J.M.: Power law burst lifetime distribution of the SYM-H index. Geophys. Res. Lett. 34(L04107) (2007). https://doi.org/10.1029/2006gl028235
Liu, M.-J., Dobias, P., Eisler, C.: Fractal patterns in coastal detections on approaches to Canada. J. Appl. Oper. Res. 7(2), 80–95 (2015)
Horn, S., Eisler, C., Dobias, P., Collins, J.: Data requirements for anomaly detection. In: Proceedings from Maritime Knowledge Discovery and Anomaly Detection, Italy (2016)
Shi, F., Chen, Q.J., Li, N.N.: Hilbert Huang transform for predicting proteins subcellular location. J. Biomed. Sci. Eng. 1, 59–63 (2008)
Dobias, P.: Self-organized criticality in asymmetric warfare. Fractals 17(1), 91–97 (2009)
Sprague, K., Dobias, P., Bryce, R.: Critical behaviour in patterns of violence in Afghanistan. J. Battlefield Technol. 14(1), 17–24 (2011)
Dobias, P., Wanliss, J.A.: Fractal properties of conflict in Afghanistan revisited. J. Battlefield Technol. 15(3), 31–36 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Crown
About this paper
Cite this paper
Dobias, P., Wanliss, J.A. (2018). Empirical Mode Decomposition in Defence Data Analysis. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-96661-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96660-1
Online ISBN: 978-3-319-96661-8
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)