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Sensors 2018, 18(8), 2621; https://doi.org/10.3390/s18082621

Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average

1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
Beijing Aerospace Automatic Control Institute, Beijing 100854, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 7 July 2018 / Revised: 1 August 2018 / Accepted: 7 August 2018 / Published: 10 August 2018
(This article belongs to the Section Physical Sensors)
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Abstract

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments. View Full-Text
Keywords: future trend forecast; empirical wavelet transform; autoregressive moving average model future trend forecast; empirical wavelet transform; autoregressive moving average model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, Q.; Li, H.; Lin, J.; Zhang, C. Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average. Sensors 2018, 18, 2621.

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