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.
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 language | English |
|---|---|
| Pages (from-to) | 92-112 |
| Number of pages | 21 |
| Journal | International Journal of Management and Decision Making |
| Volume | 21 |
| Issue number | 1 |
| Early online date | 2 Dec 2021 |
| DOIs | |
| Publication status | Published - 1 Jan 2022 |