IDEAS home Printed from https://ideas.repec.org/p/cyb/wpaper/2015-1.html
   My bibliography  Save this paper

Estimation and Prediction of Shipping Trends with the Data-Driven Haar-Fisz Transform

Author

Listed:
  • Antonis A. Michis

    (Central Bank of Cyprus)

  • Guy P. Nason

    (School of Mathematics, University of Bristol, University Walk)

Abstract

We describe the implementation of a computer-based automatic procedure to estimate the trends associated with debit and credit transaction flows in Cyprus’s shipping industry. The procedure was also extended to forecasting. Transactions in the shipping industry do not always coincide with the time the service is provided. The transactions are usually completed gradually throughout the financial year and occasionally involve large amounts for balance settlements. In addition, the transactions are subject to several market risks such as the freight rate and exchange rate changes. Consequently, the transactions frequently exhibit large values and changes in variance, which makes trend estimation and forecasting difficult. A key component of the procedure we implemented is a variance stabilization method based on the Data-Driven Haar-Fisz Transform that enables accurate estimation of trends in volatile time series data. This method is sufficiently flexible to accommodate data characteristics such as cyclical changes, shifts in trend and spikes that are frequently encountered in transaction flow data.

Suggested Citation

  • Antonis A. Michis & Guy P. Nason, 2015. "Estimation and Prediction of Shipping Trends with the Data-Driven Haar-Fisz Transform," Working Papers 2015-1, Central Bank of Cyprus.
    • Handle: RePEc:cyb:wpaper:2015-1
    as

    Download full text from publisher

    File URL: https://www.centralbank.cy/images/media/pdf/Working_Paper_2015-01.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fryzlewicz, Piotr & Delouille, V´eronique & Nason, Guy P., 2007. "GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar-Fisz transform," LSE Research Online Documents on Economics 25221, London School of Economics and Political Science, LSE Library.
    2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    3. Piotr Fryzlewicz & Véronique Delouille & Guy P. Nason, 2007. "GOES‐8 X‐ray sensor variance stabilization using the multiscale data‐driven Haar–Fisz transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(1), pages 99-116, January.
    4. Antonis A Michis, 2015. "A wavelet smoothing method to improve conditional sales forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 832-844, May.
    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. 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.
    2. Antonis A. Michis, 2021. "Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 443-480, October.
    3. Guy P. Nason & Daniel Bailey, 2008. "Estimating the intensity of conflict in Iraq," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 899-914, October.
    4. 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.
    5. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    6. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    7. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    8. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    9. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    10. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    11. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    12. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    13. Niematallah Elamin & Mototsugu Fukushige, 2016. "Forecasting extreme seasonal tourism demand," Discussion Papers in Economics and Business 16-23, Osaka University, Graduate School of Economics.
    14. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    15. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    16. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    17. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    18. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    19. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    20. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.

    More about this item

    Keywords

    Shipping; trend; wavelets; Data-Driven Haar-Fisz Transform;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    Statistics

    Access and download statistics

    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:cyb:wpaper:2015-1. 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: Anna Markidou (email available below). General contact details of provider: https://edirc.repec.org/data/cbcgvcy.html .

    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.