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On the estimation of periodic signals in the diffusion process using a high-frequency scheme

  • Getut Pramesti ORCID logo EMAIL logo and Ristu Saptono ORCID logo
Published/Copyright: November 11, 2023

Abstract

The estimation of the frequency component is very interesting to study, considering its unique nature when these parameters are together in their amplitude. The periodicity of the frequency components is also thought to affect the convergence of these parameters. In this paper, we consider the problem of estimating the frequency component of a periodic continuous-time sinusoidal signal. Under the high-frequency sampling setting, we provide the frequency components’ consistency and asymptotic normality. It is observed that the convergence rate of the continuous-time sinusoidal signal of the diffusion process is the same as the continuous-time sinusoidal signal of the Ornstein–Uhlenbeck process, which is mentioned in [G. Pramesti, Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process, Monte Carlo Methods Appl. 29 (2023), 1, 1–32]. The result of this study deduces that the convergence rate of the frequency is the same as long as the signal is periodic. In this case, the existence of the rate of reversion does not affect the convergence rate of the frequency components. Further, the result of the study, that is, the convergence rate of the frequency is ( n h ) 3 , also revised the previous one in [G. Pramesti, The least-squares estimator of sinusoidal signal of diffusion process for discrete observations, J. Math. Comput. Sci. 11 (2021), 5, 6433–6443], which mentioned ( n h ) 3 h . The proposed approach is demonstrated with a ten-minute sampling rate of real data on the energy consumption of light fixtures in one Belgium household.

MSC 2010: 92E24; 35B40; 62M10

Acknowledgements

The author is grateful to Professor Karl Sabelfeld for his remarks.

References

[1] O. Besson and P. Stoica, Nonlinear least-squares approach to frequency estimation and detection for sinusoidal signals with arbitrary envelope, Digital Signal Process. 9 (1999), no. 1, 45–56. 10.1006/dspr.1998.0330Search in Google Scholar

[2] J. Brownlee, Machine Learning Mastery with Python: Understand your Data, Create Accurate Models, and work Projects End-to-End, Machine Learning Mastery, 2016. Search in Google Scholar

[3] L. M. Candanedo, V. Feldheim and D. Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy Buildings 140 (2017), 81–97. 10.1016/j.enbuild.2017.01.083Search in Google Scholar

[4] V. Genon-Catalot and J. Jacod, On the estimation of the diffusion coefficient for multi-dimensional diffusion processes, Ann. Inst. H. Poincaré Probab. Statist. 29 (1993), no. 1, 119–151. Search in Google Scholar

[5] S. M. Iacus, Simulation and Inference for Stochastic Differential Equations, Springer Ser. Statist., Springer, New York, 2009. 10.1007/978-0-387-75839-8Search in Google Scholar

[6] I. A. Ibragimov and R. Z. Has’minskii, Several estimation problems in a Gaussian white noise, Statistical Estimation, Springer, New York (1981), 321–361. 10.1007/978-1-4899-0027-2_9Search in Google Scholar

[7] L. Igual and S. Seguí, Introduction to Data Science, Undergrad. Top. Comput. Sci, Springer, Cham, 2017. 10.1007/978-3-319-50017-1Search in Google Scholar

[8] D. Kundu, Asymptotic theory of least squares estimator of a particular nonlinear regression model, Statist. Probab. Lett. 18 (1993), no. 1, 13–17. 10.1016/0167-7152(93)90093-XSearch in Google Scholar

[9] S. Nandi and D. Kundu, Statistical Signal Processing—Frequency Estimation, Springer,Singapore, 2012. Search in Google Scholar

[10] B. Øksendal, Stochastic Differential Equations: An Introduction with Applications, Universitext, Springer, Berlin, 2013. Search in Google Scholar

[11] G. Pramesti, The least-squares estimator of sinusoidal signal of diffusion process for discrete observations, J. Math. Comput. Sci. 11 (2021), no. 5, 6433–6443. Search in Google Scholar

[12] G. Pramesti, Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process, Monte Carlo Methods Appl. 29 (2023), no. 1, 1–32. 10.1515/mcma-2022-2127Search in Google Scholar

[13] P. Stoica, T. Söderström and F. N. Ti, Asymptotic properties of the high-order Yule–Walker estimates of sinusoidal frequencies, IEEE Trans. Acoust. Speech Signal Process. 37 (1989), no. 11, 1721–1734. 10.1109/29.46554Search in Google Scholar

[14] A. M. Walker, On the estimation of a harmonic component in a time series with stationary independent residuals, Biometrika 58 (1971), 21–36. 10.1093/biomet/58.1.21Search in Google Scholar

[15] P. Whittle, The simultaneous estimation of a time series harmonic components and covariance structure, Trabajos Estadíst. 3 (1952), 43–57. 10.1007/BF03002861Search in Google Scholar

[16] I. Ziskind and M. Wax, Maximum likelihood localization of multiple sources by alternating projection, IEEE Trans Acoust. Speech Signal Process. 36 (1988), no. 10, 1553–1560. 10.1109/29.7543Search in Google Scholar

[17] UCI Machine Learning Repository, Belgium household energy prediction. Search in Google Scholar

Received: 2023-03-14
Revised: 2023-10-01
Accepted: 2023-10-02
Published Online: 2023-11-11
Published in Print: 2024-03-01

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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