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Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average.

Qiusheng Wang1, Haipeng Li2, Jinyong Lin3

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China. wangqiusheng@buaa.edu.cn.

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|August 15, 2018
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Summary
This summary is machine-generated.

This study introduces a new method for predicting complex system trends using empirical wavelet transform (EWT) and autoregressive moving average (ARMA) models. The approach effectively forecasts future operational behaviors by analyzing sensor signal modes.

Keywords:
autoregressive moving average modelempirical wavelet transformfuture trend forecast

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Area of Science:

  • Engineering
  • Signal Processing
  • System Monitoring

Background:

  • Complex systems generate numerous sensor signals with indirect relationships, reflecting overall operational states.
  • Accurate evaluation and prediction of system behavior are crucial for field engineers.
  • Existing methods may not fully capture the nuanced information within these complex signals.

Purpose of the Study:

  • To propose a novel method for forecasting future operational trends in complex systems.
  • To enhance the predictive capabilities for system behavior analysis.
  • To provide engineers with a reliable tool for evaluating and predicting system states.

Main Methods:

  • Empirical Wavelet Transform (EWT) is employed to extract significant modes from sensor signals.
  • System states are represented using indicator functions derived from normalized and weighted significant modes.
  • Autoregressive Moving Average (ARMA) models are utilized for parametric forecasting of future trends.

Main Results:

  • The EWT effectively isolates key signal components reflecting system aspects.
  • The indicator function provides a robust representation of system states.
  • ARMA modeling successfully forecasts future operational trends based on extracted modes.

Conclusions:

  • The proposed EWT-ARMA method offers an effective and practical approach for complex system trend forecasting.
  • This technique enhances the ability to evaluate and predict the future behavior of monitored systems.
  • Numerical experiments validate the efficacy and applicability of the developed forecasting method.