Case study: shipping trend estimation and prediction via multiscale variance stabilisation

Antonis A. Michis, Guy P. Nason*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
284 Downloads (Pure)


Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.

Original languageEnglish
Pages (from-to)2672-2684
Number of pages13
JournalJournal of Applied Statistics
Issue number15
Early online date24 Nov 2016
Publication statusPublished - 18 Nov 2017


  • Data-driven Haar–Fisz transform
  • heteroscedastic
  • shipping creditflow
  • trend estimation


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