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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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)

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    Keywords

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    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

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