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Real time prediction of irregular periodic time series data

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  • Kaimeng Zhang
  • Chi Tim Ng
  • Myung Hwan Na

Abstract

By means of a novel time‐dependent cumulated variation penalty function, a new class of real‐time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns: in particular, the breathing motion data of the patients during robotic radiation therapy. It is illustrated that for both simulated and empirical data involving changes in mean, trend, and amplitude, the proposed methods outperform existing forecasting methods based on support vector machines and artificial neural network in terms of prediction accuracy. Moreover, the proposed methods are designed so that real‐time updates can be done efficiently with O(1) computational complexity upon the arrival of a new signal without scanning the old data repeatedly.

Suggested Citation

  • Kaimeng Zhang & Chi Tim Ng & Myung Hwan Na, 2020. "Real time prediction of irregular periodic time series data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 501-511, April.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:3:p:501-511
    DOI: 10.1002/for.2637
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    1. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    2. Ngai Hang Chan & Chun Yip Yau & Rong-Mao Zhang, 2014. "Group LASSO for Structural Break Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 590-599, June.
    3. Chen, Bei & Gel, Yulia R., 2010. "Autoregressive frequency detection using Regularized Least Squares," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1712-1727, August.
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    Cited by:

    1. Marat Akhmet & Madina Tleubergenova & Akylbek Zhamanshin, 2023. "Shunting Inhibitory Cellular Neural Networks with Compartmental Unpredictable Coefficients and Inputs," Mathematics, MDPI, vol. 11(6), pages 1-18, March.

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