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

Antonis Michis () and Guy P. Nason ()
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Guy P. Nason: School of Mathematics, University of Bristol, University Walk

No 2015-1, Working Papers from Central Bank of Cyprus

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.

Keywords: Shipping; trend; wavelets; Data-Driven Haar-Fisz Transform (search for similar items in EconPapers)
JEL-codes: C44 C53 C87 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2015-08
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