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Multiway Similarity Approach Based on Divergence Functions and Smoothness Measure

100%
Szupiluk R., Sobon T.
Acta Physica Polonica A
|
2016
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vol. 129
|
issue 5
927-931
EN
In this paper we present a novel similarity measure method for financial data. In our approach, we propose the assessment of the similarity in a coherent hierarchical and multi-faceted way, following the general scheme where various detailed basic measures may be used like the Fermi-Dirac divergence, Bose-Einstein divergence, or our new smoothness measure. The presented method is tested on benchmark and real stock markets data.
2
Content available remote

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

81%
Szupiluk R., Ząbkowski T., Soboń T.
Acta Physica Polonica A
|
2016
|
vol. 129
|
issue 5
1018-1022
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.
3
Content available remote

Independent Component Analysis for Ensemble Predictors with Small Number of Models

81%
Szupiluk R., Ząbkowski T., Gajowniczek K.
Acta Physica Polonica A
|
2015
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vol. 127
|
issue 3A
A-139-A-144
EN
The article presents independent component analysis (ICA) applied to the concept of ensemble predictors. The use of ICA decomposition enables to extract components with particular statistical properties that can be interpreted as destructive or constructive for the prediction. Such process can be treated as noise filtration from multivariate observation data, in which observed data consist prediction results. As a consequence of the ICA multivariate approach, the final results are combination of the primary models, what can be interpreted as aggregation step. The key issue of the presented method is the identification of noise components. For this purpose, a new method for evaluating the randomness of the signals was developed. The experimental results show that presented approach is effective for ensemble prediction taking into account different prediction criteria and even small set of models.
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