IDEAS home Printed from https://ideas.repec.org/p/tiu/tiucen/94542b5e-4319-4f5a-bc35-258e532d573c.html
   My bibliography  Save this paper

Robust Forecasting of Non-Stationary Time Series

Author

Listed:
  • Croux, C.
  • Fried, R.
  • Gijbels, I.
  • Mahieu, K.

Abstract

No abstract is available for this item.

Suggested Citation

  • Croux, C. & Fried, R. & Gijbels, I. & Mahieu, K., 2010. "Robust Forecasting of Non-Stationary Time Series," Discussion Paper 2010-105, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:94542b5e-4319-4f5a-bc35-258e532d573c
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/1269500/2010-105.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. I. Gijbels & A. Pope & M. P. Wand, 1999. "Understanding exponential smoothing via kernel regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 39-50.
    2. Fried, Roland & Einbeck, Jochen & Gather, Ursula, 2007. "Weighted Repeated Median Smoothing and Filtering," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1300-1308, December.
    3. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Croux, C. & Fried, R. & Gijbels, I. & Mahieu, K., 2010. "Robust Forecasting of Non-Stationary Time Series," Other publications TiSEM 94542b5e-4319-4f5a-bc35-2, Tilburg University, School of Economics and Management.
    2. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    3. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    4. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    5. Ralf Becker & Adam Clements & Robert O'Neill, 2018. "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns," Econometrics, MDPI, vol. 6(1), pages 1-27, February.
    6. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Kernel Technique for Forecasting the Variance-Covariance Matrix," Centre for Growth and Business Cycle Research Discussion Paper Series 151, Economics, The University of Manchester.
    7. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    8. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    9. Chia-Nan Wang & Jen-Der Day & Nguyen Thi Kim Lien & Luu Quoc Chien, 2018. "Integrating the Additive Seasonal Model and Super-SBM Model to Compute the Efficiency of Port Logistics Companies in Vietnam," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    10. Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
    11. Steven D. Silver & Marko Raseta, 2021. "An ARFIMA multi-level model of dual-component expectations in repeated cross-sectional survey data," Empirical Economics, Springer, vol. 60(2), pages 683-699, February.
    12. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
    13. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    14. Hart, Jeffrey D. & Lee, Cherng-Luen, 2005. "Robustness of one-sided cross-validation to autocorrelation," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 77-96, January.
    15. E. Bajalinov & Sz. Duleba, 2020. "Seasonal time series forecasting by the Walsh-transformation based technique," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 983-1001, September.
    16. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    17. H. Kent Baker & Satish Kumar & Debidutta Pattnaik, 2021. "Research constituents, intellectual structure, and collaboration pattern in the Journal of Forecasting: A bibliometric analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 577-602, July.
    18. Boswijk, H. P. & Zu, Y., 2013. "Testing for Cointegration with Nonstationary Volatility," Working Papers 13/08, Department of Economics, City University London.
    19. Jungwoo Kim & Joocheol Kim, 2017. "Nonparametric forecasting with one-sided kernel adopting pseudo one-step ahead data," Working papers 2017rwp-102, Yonsei University, Yonsei Economics Research Institute.
    20. James Brooks & Irena Grugulis & Hugh Cook, 2020. "Rethinking Situated Learning: Participation and Communities of Practice in the UK Fire and Rescue Service," Work, Employment & Society, British Sociological Association, vol. 34(6), pages 1045-1061, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tiu:tiucen:94542b5e-4319-4f5a-bc35-258e532d573c. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richard Broekman (email available below). General contact details of provider: http://center.uvt.nl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.