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The wavelet detection of hidden periodicities in time series

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  • Li, Yuan
  • Xie, Zhongjie

Abstract

A wavelet method is applied to the detection of the hidden periodicities of a hidden periodic model in time series. By checking the empirical wavelet coefficients of the periodogram, which have significantly large absolute values across fine scale levels, the number of the hidden periodicities in the model is identified. Estimators of the hidden periodicities are given, and shown to be strongly consistent.

Suggested Citation

  • Li, Yuan & Xie, Zhongjie, 1997. "The wavelet detection of hidden periodicities in time series," Statistics & Probability Letters, Elsevier, vol. 35(1), pages 9-23, August.
    • Handle: RePEc:eee:stapro:v:35:y:1997:i:1:p:9-23
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    Cited by:

    1. Ip, Wai-Cheung & Wong, Heung & Li, Yuan & Xie, Zhongjie, 1999. "Threshold variable selection by wavelets in open-loop threshold autoregressive models," Statistics & Probability Letters, Elsevier, vol. 42(4), pages 375-392, May.
    2. Wong, Heung & Ip, Waicheung & Li, Yuan, 2001. "Detection of jumps by wavelets in a heteroscedastic autoregressive model," Statistics & Probability Letters, Elsevier, vol. 52(4), pages 365-372, May.
    3. Shin, Dong Wan, 2004. "Estimation of spectral density for seasonal time series models," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 149-159, April.

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