Hull-WEMA: a novel zero-lag approach in the moving average family, with an application to COVID-19

Seng Hansun, Vincent Charles*, Tatiana Gherman, Vijayakumar Varadarajan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The moving average (MA) is undeniably one of the most popular forecasting methods in time series analysis. In this study, we consider two variants of MA, namely the weighted exponential moving average (WEMA) and the hull moving average (HMA). WEMA, which was introduced in 2013, has been widely used in different scenarios but still suffers from lags. To address this shortcoming, we propose a novel zero-lag Hull-WEMA method that combines HMA and WEMA. We apply and compare the proposed approach with HMA and WEMA by using COVID-19 time series data from ten different countries with the highest number of cases on the last observed date. Results show that the new approach achieves a better accuracy level than HMA and WEMA. Overall, the paper advocates a white-box forecasting method, which can be used to predict the number of confirmed COVID-19 cases in the short run more accurately.

Original languageEnglish
Pages (from-to)92-112
Number of pages21
JournalInternational Journal of Management and Decision Making
Issue number1
Early online date09 Dec 2021
Publication statusPublished - 01 Jan 2022
Externally publishedYes


  • COVID-19
  • HMA
  • Hull moving average
  • Hull-WEMA
  • Moving average
  • Python 3
  • Time series forecasting
  • Weighted exponential moving average WEMA
  • White-box model

ASJC Scopus subject areas

  • General Decision Sciences


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