Analysis of Financial Time Series Morphology with AMUSE Algorithm and Its Extensions

• Authors: R. Szupiluk , T. Ząbkowski , T. Soboń
• Languages of publication: EN

Abstracts

EN

The proposed article presents a new approach to analyze the relationships between financial instruments. We use blind signal separation methods to decompose time series into the core components. The components common to the various instruments provide broad set of characteristics to describe the internal morphology of the time series. In this research a modified and extended version of AMUSE algorithm is used. The concept is presented based on real financial instruments.

Keywords

EN

05.45.Tp; 05.40.Ca; 07.05.Kf; 07.05.Mh

Discipline

• 05.40.Ca: Noise
• 05.45.Tp: Time series analysis
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence
• 07.05.Kf: Data analysis: algorithms and implementation; data management(for data analysis in nuclear physics, see 29.85.-c)

• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 129
• Issue: 5
• Pages: 1018-1022

Dates

published

2016-05

Contributors

author

R. Szupiluk

• Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland

author

T. Ząbkowski

• Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland

author

T. Soboń

• Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland

References

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• [8] W. Perks, J. Inst. Actuar. 63, 1257 (1932)
• [9] R. Szupiluk, Multivariate decompositions for predictive Data Mining models aggregation, Oficyna Wydawnicza Szkoły Głównej Handlowej w Warszawie, Warszawa 2013 (in Polish)
• [10] R. Szupiluk, P. Wojewnik, T. Zabkowski, Lect. Notes Comp. Sci. 6594, 133 (2006), doi: 10.1007/11840930_14

Identifiers

DOI

10.12693/APhysPolA.129.1018