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

Abstract

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
Volume21
Issue number1
Early online date2 Dec 2021
DOIs
Publication statusPublished - 1 Jan 2022

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