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Forecasting non-stationary time series by wavelet process modelling

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  • Piotr Fryzlewicz
  • Sébastien Bellegem
  • Rainer Sachs

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  • Piotr Fryzlewicz & Sébastien Bellegem & Rainer Sachs, 2003. "Forecasting non-stationary time series by wavelet process modelling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 737-764, December.
    • Handle: RePEc:spr:aistmt:v:55:y:2003:i:4:p:737-764
    DOI: 10.1007/BF02523391
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    References listed on IDEAS

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    1. Ombao H. C & Raz J. A & von Sachs R. & Malow B. A, 2001. "Automatic Statistical Analysis of Bivariate Nonstationary Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 543-560, June.
    2. Dahlhaus, R. & Neumann, M. & Von Sachs, R., 1997. "Nonlinear Wavelet Estimation of Time-Varying Autoregressive Processes," SFB 373 Discussion Papers 1997,34, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Nason, G.P. & von Sachs, R., 1999. "Wavelets in Time Series Analysis," Papers 9901, Catholique de Louvain - Institut de statistique.
    4. Dahlhaus, R., 1996. "On the Kullback-Leibler information divergence of locally stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 139-168, March.
    5. Antoniadis, Anestis & Sapatinas, Theofanis, 2003. "Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 133-158, October.
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    Citations

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    Cited by:

    1. I A Eckley & G P Nason, 2018. "A test for the absence of aliasing or local white noise in locally stationary wavelet time series," Biometrika, Biometrika Trust, vol. 105(4), pages 833-848.
    2. Schlüter, Stephan & Deuschle, Carola, 2010. "Using wavelets for time series forecasting: Does it pay off?," FAU Discussion Papers in Economics 04/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    4. Guy Nason & Kara Stevens, 2015. "Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-24, September.
    5. Guy Nason, 2013. "A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 879-904, November.
    6. Fryzlewicz, Piotr & Nason, Guy P., 2004. "Smoothing the wavelet periodogram using the Haar-Fisz transform," LSE Research Online Documents on Economics 25231, London School of Economics and Political Science, LSE Library.
    7. Joanna Bruzda, 2020. "The wavelet scaling approach to forecasting: Verification on a large set of Noisy data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 353-367, April.
    8. Antonis A. Michis & Guy P. Nason, 2017. "Case study: shipping trend estimation and prediction via multiscale variance stabilisation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2672-2684, November.
    9. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent classification of non-stationary time series using stochastic wavelet representations," LSE Research Online Documents on Economics 25162, London School of Economics and Political Science, LSE Library.
    10. Euan T. McGonigle & Rebecca Killick & Matthew A. Nunes, 2022. "Trend locally stationary wavelet processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 895-917, November.
    11. Dominique Guegan, 2008. "Non-stationarity and meta-distribution," Post-Print halshs-00270708, HAL.
    12. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
    13. Marios Sergides & Efstathios Paparoditis, 2009. "Frequency Domain Tests of Semiparametric Hypotheses for Locally Stationary Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 800-821, December.
    14. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
    15. Holger Dette & Weichi Wu, 2020. "Prediction in locally stationary time series," Papers 2001.00419, arXiv.org, revised Jan 2020.
    16. Triantafyllopoulos, K. & Nason, G.P., 2009. "A note on state space representations of locally stationary wavelet time series," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 50-54, January.
    17. Tata Subba Rao & Granville Tunnicliffe Wilson & Alessandro Cardinali & Guy P. Nason, 2017. "Locally Stationary Wavelet Packet Processes: Basis Selection and Model Fitting," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 151-174, March.

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