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A Method for Fast and Robust Sungular Spectrum Analysis

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Mathematical Modeling in Physical Sciences (ICMSQUARE 2023)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 446))

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Abstract

One of the main problems encountered in the analysis of real-world time series, is the existence of outliers originating from various sources other than the dynamics of the series. Such sources can be a momentary noise or an imperfection in the recording devices. This problem has led to the need to construct robust methods using metrics other than the usual mean squared error. In combination with non-parametric techniques such as the very popular Singular Value Decomposition (SSA), it can give iterative algorithms that deal with this problem but create huge needs in computing power, especially in problems where we need fast response of the system (for example in high frequency trading). In this paper, a technique addressing both problems is presented which exploits the peculiarity of the Singular Value Decomposition (SVD) analysis of square symmetric matrices.

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References

  1. Alexandros, N.K., Sakas, D.P., Vlachos, D.S., Dimitrios, N.K.: Comparing scrum and xp agile methodologies using dynamic simulation modeling. In: Strategic Innovative Marketing: 5th IC-SIM, Athens, Greece 2016, pp. 391–397. Springer International Publishing (2017)

    Google Scholar 

  2. Bond, G., Kromer, B., Beer, J., Muscheler, R., Evans, M.N., Showers, W., Hoffmann, S., Lotti-Bond, R., Hajdas, I., Bonani, G.: Persistent solar influence on north Atlantic climate during the Holocene. Science 294(5549), 2130–2136 (2001)

    Google Scholar 

  3. Croux, C., Filzmoser, P., Rosario Oliveira, M.: Algorithms for projection–pursuit robust principal component analysis. Chem. Intell. Laboratory Syst. 87(2), 218–225 (2007)

    Google Scholar 

  4. Croux, Christophe, Ruiz-Gazen, Anne: High breakdown estimators for principal components: the projection-pursuit approach revisited. J. Multivariate Anal. 95(1), 206–226 (2005)

    Article  MathSciNet  Google Scholar 

  5. Dimitrios, N.K., Sakas, D.P., Vlachos, D.S.: The contribution of dynamic simulation model of depiction of knowledge, in the leading process of high technology companies. Key Engineering Mater. 543, 406–409 (2013)

    Google Scholar 

  6. Dimitrios, N.K., Sakas, D.P., Vlachos, D.S.: Modeling publications in academic conferences. Procedia-Soc. Behav. Sci. 147, 467–477 (2014)

    Google Scholar 

  7. Filzmoser, Peter, Todorov, Valentin: Robust tools for the imperfect world. Inf. Sci. 245, 4–20 (2013)

    Article  MathSciNet  Google Scholar 

  8. Golyandina, N., Nekrutkin V., Zhigljavsky, A.A.: Analysis of time series structure: SSA and related techniques. CRC Press (2001)

    Google Scholar 

  9. Hassani, Hossein, Thomakos, Dimitrios: A review on singular spectrum analysis for economic and financial time series. Stat. Interface 3(3), 377–397 (2010)

    Article  MathSciNet  Google Scholar 

  10. Hawkins, D., Liu, L., Stanley Young, S.: Robust singular value decomposition technical report number 122 (2001). National Institute of Statistical Sciences 19 (2002)

    Google Scholar 

  11. Hubert, M., Rousseeuw, P.J., Branden, K.V.: Robpca: a new approach to robust principal component analysis. Technometrics 47(1), 64–79 (2005)

    Google Scholar 

  12. Kosmas, O.T., Vlachos, D.S.: Phase-fitted discrete lagrangian integrators. Comput. Phys. Commun. 181(3), 562–568 (2010)

    Google Scholar 

  13. Kosmas, O.T., Vlachos, D.S.: Local path fitting: a new approach to variational integrators. J. Comput. Appl. Math. 236(10), 2632–2642 (2012)

    Google Scholar 

  14. Mahmoudvand, R., Konstantinides, D., Rodrigues, P.C.: Forecasting mortality rate by multivariate singular spectrum analysis. Appl. Stoch. Models Bus. Ind. 33(6), 717–732 (2017)

    Google Scholar 

  15. Muruganatham, B., Sanjith, M.A., Krishnakumar, B., Murty, S.S.: Roller element bearing fault diagnosis using singular spectrum analysis. Mech. Syst. Signal Process. 35(1-2), 150–166 (2013)

    Google Scholar 

  16. Nasiopoulos, D.K., Sakas, D.P., Vlachos, D.S., Mavrogianni, A.: Modeling of market segmentation for new it product development. In: AIP Conference Proceedings, vol. 1644, pp. 51–59. American Institute of Physics (2015)

    Google Scholar 

  17. Nasiopoulos, D.K., Sakas, D.P., Vlachos, D.S., Mavrogianni, A.: Simulation of generation of new ideas for new product development and it services. In: AIP Conference Proceedings, vol. 1644, pp. 60–68. American Institute of Physics (2015)

    Google Scholar 

  18. Rodrigues, P.C., De Carvalho, M.: Spectral modeling of time series with missing data. Appl. Math. Model. 37(7), 4676–4684 (2013)

    Google Scholar 

  19. Paulo Canas Rodrigues and Rahim Mahmoudvand: Correlation analysis in contaminated data by singular spectrum analysis. Quality Reliab. Eng. Int. 32(6), 2127–2137 (2016)

    Article  Google Scholar 

  20. Sakas, D.P., Vlachos, D.S., Nasiopoulos, D.K.: Modeling the development of the online conference’s services. Library Rev. 65(3), 160–184 (2016)

    Google Scholar 

  21. Salgado, D.R., Alonso, F.J.: Tool wear detection in turning operations using singular spectrum analysis. J. Mater. Process. Technol. 171(3), 451–458 (2006)

    Article  Google Scholar 

  22. Schlesinger, M.E., Ramankutty, N.: An oscillation in the global climate system of period 65–70 years. Nature 367(6465), 723–726 (1994)

    Google Scholar 

  23. Skafidas, P.D., Vlachos, D.S., Avaritsiotis, J.N.: Modelling and simulation of abnormal behaviour of thick-film tin oxide gas sensors in co. Sensors Actuators B: Chem. 21(2), 109–121 (1994)

    Article  Google Scholar 

  24. Vlachos, D.S.: Optimal ship routing based on wind and wave forecasts. Appl. Numer. Anal. Comput. Math. 1(2), 547–551 (2004)

    Article  MathSciNet  Google Scholar 

  25. Vlachos, D.S.: Self-calibration techniques of underwater gamma ray spectrometers. J. Environ. Radioact. 82(1), 21–32 (2005)

    Article  Google Scholar 

  26. Vlachos, D.S., Papadopoulos, C.A., Avaritsiotis, J.N.: A technique for suppressing ethanol interference employing seebeck effect devices with carrier concentration modulation. Sensors and Actuators B: Chem. 44(1–3), 239–242 (1997)

    Article  Google Scholar 

  27. Vlachos, D.S., Simos, T.E.: Partitioned linear multistep method for long term integration of the n-body problem. Appl. Numer. Anal. Comput. Math. 1(2), 540–546 (2004)

    Article  MathSciNet  Google Scholar 

  28. Vlachos, D.S., Skafidas, P.D., Avaritsiotis, J.N.: Transient effects of tin oxide co sensors in the presence of water vapor. Appl. Phys. Lett. 63(13), 1760–1761 (1993)

    Article  Google Scholar 

  29. Vlachos, D.S., Xenoulis, A.C.: Gas detection sensitivity and cluster size. Nanostruct. Mater. 10(8), 1355–1361 (1998)

    Article  Google Scholar 

  30. Wu, A., Hsieh, W.W., Tang, B.: Neural network forecasts of the tropical pacific sea surface temperatures. Neural Netw. 19(2), 145–154 (2006)

    Google Scholar 

  31. Zhigljavsky, Anatoly: Singular spectrum analysis for time series: introduction to this special issue. Stat. Interface 3(3), 255–258 (2010)

    Article  MathSciNet  Google Scholar 

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Correspondence to Adamantia Mavrogianni .

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Mavrogianni, A. (2024). A Method for Fast and Robust Sungular Spectrum Analysis. In: Vlachos, D. (eds) Mathematical Modeling in Physical Sciences. ICMSQUARE 2023. Springer Proceedings in Mathematics & Statistics, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-52965-8_3

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