Abstract
The paper presents a new method for randomness assessment in data with temporal structure. In this approach we perform multistage covariance analysis on several parts of the signal to synthesize information about variability and internal dependencies included in its structure. This allows us to identify deterministic cycles or to detect the level of randomness in signals what is an important issue for the design of transactional, prediction and filtration systems. To confirm validity of the proposed method we tested it on simulated and real financial time series.
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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2011). Multistage Covariance Approach to Measure the Randomness in Financial Time Series Analysis. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_63
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DOI: https://doi.org/10.1007/978-3-642-22000-5_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21999-3
Online ISBN: 978-3-642-22000-5
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