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Detecting low-dimensional chaos by the “noise titration” technique: Possible problems and remedies

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  • Gao, Jianbo
  • Hu, Jing
  • Mao, Xiang
  • Tung, Wen-wen

Abstract

Distinguishing low-dimensional chaos from noise is an important issue in time series analysis. Among the many methods proposed for this purpose is the noise titration technique, which quantifies the amount of noise that needs to be added to the signal to fully destroy its nonlinearity. Two groups of researchers recently have questioned the validity of the technique. In this paper, we report a broad range of situations where the noise titration technique fails, and offer solutions to fix the problems identified.

Suggested Citation

  • Gao, Jianbo & Hu, Jing & Mao, Xiang & Tung, Wen-wen, 2012. "Detecting low-dimensional chaos by the “noise titration” technique: Possible problems and remedies," Chaos, Solitons & Fractals, Elsevier, vol. 45(3), pages 213-223.
    • Handle: RePEc:eee:chsofr:v:45:y:2012:i:3:p:213-223
    DOI: 10.1016/j.chaos.2011.12.004
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    Cited by:

    1. He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.

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