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A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

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  • Chaolong Jia
  • Lili Wei
  • Hanning Wang
  • Jiulin Yang

Abstract

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

Suggested Citation

  • Chaolong Jia & Lili Wei & Hanning Wang & Jiulin Yang, 2015. "A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, March.
    • Handle: RePEc:hin:jnlmpe:548720
    DOI: 10.1155/2015/548720
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