A novel hybrid Holt’s Method Integrated Moving Average (HIMA)

This research tackles the challenge of forecasting future values, which is crucial as inaccurate predictions can lead to poor decisionmaking. Thus, this study develops a new hybrid forecasting model, the Holt Integrated Moving Average (HIMA), which combines Holt’s method and the Moving Average technique from the Box-Jenkins methodology. This model aims to enhance the accuracy of forecasts in time-series data that exhibit trends and levels. The effectiveness of the HIMA model was tested using four time-series datasets: the Consumer Price Index (CPI), air pollution (PM2.5), and tourist arrivals in Malaysia. The findings demonstrate that the HIMA model significantly outperforms other univariate models, achieving forecast accuracies between 95 to 98 percent.