Evaluating Time-Series Models for Dengue Haemorrhagic Fever Prediction: Arima vs. Sarima and Exponential Smoothing

Accurately forecasting the transmission of infectious diseases, such as Dengue Haemorrhagic Fever (DHF), is essential for robust public health planning. This study assesses the performance of three time-series forecasting models—Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA)—in predicting annual DHF cases in Malaysia from 2011 to 2021. Model accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). Results indicate that ARIMA and SARIMA yielded the lowest error rates (73.34%), whereas Exponential Smoothing performed less effectively (76.79%). ARIMA was preferred due to its simplicity and comparable predictive power. Although these error rates exceed the standard 10–20% threshold for reliable forecasting, the study demonstrates that these models have significant potential when integrated with more granular data and external covariates. Furthermore, a three-segment piecewise nonlinear regression was employed to identify structural changes in DHF trends. This combined analytical approach provides deeper insights into disease progression and strengthens the evidence base for effective public health interventions.

ARIMA, curve fitting, dengue forecasting, exponential smoothing

1. M. G. Guzman, M. C. Marqueti, E. Martinez, and A. B. Perez, "Dengue, Dengue hemorrhagic fever," in International Encyclopedia of Public Health (Third Edition), S. R. Quah Ed. Oxford: Academic Press, 2025, pp. 275-319. [Google Scholar] [Crossref]

2. D. S. Shepard, E. A. Undurraga, and Y. A. Halasa, "Economic and disease burden of dengue in Southeast Asia," PLoS neglected tropical diseases, vol. 7, no. 2, p. e2055, 2013. [Google Scholar] [Crossref]

3. R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2018. [Google Scholar] [Crossref]

4. S. Makridakis, R. J. Hyndman, and F. Petropoulos, "Forecasting in social settings: The state of the art," International Journal of Forecasting, vol. 36, no. 1, pp. 15-28, 2020/01/01/ 2020, doi: https://doi.org/10.1016/j.ijforecast.2019.05.011. [Google Scholar] [Crossref]

5. P. Hasan, T. D. Khan, I. Alam, and M. E. Haque, "Dengue in Tomorrow: Predictive Insights From ARIMA and SARIMA Models in Bangladesh: A Time Series Analysis," Health Science Reports, Article vol. 7, no. 12, 2024, Art no. e70276, doi: 10.1002/hsr2.70276. [Google Scholar] [Crossref]

6. V. J. Jayaraj, R. Avoi, N. Gopalakrishnan, D. B. Raja, and Y. Umasa, "Developing a dengue prediction model based on climate in Tawau, Malaysia," Acta Tropica, vol. 197, p. 105055, 2019/09/01/ 2019, doi: https://doi.org/10.1016/j.actatropica.2019.105055. [Google Scholar] [Crossref]

7. M. MoH, "Clinical Practice Guidelines: Management of Dengue Infection in Adults," ed: Putrajaya, Malaysia: Ministry of Health Malaysia, 2015. [Google Scholar] [Crossref]

8. L. Kumar, K. Sharma, and U. K. Khedlekar, "Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method," Results in Control and Optimization, vol. 15, p. 100432, 2024/06/01/ 2024, doi: https://doi.org/10.1016/j.rico.2024.100432. [Google Scholar] [Crossref]

9. G. CANTARELLA, C. FIORI, and P. VELONÀ, "Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: fixed-point stability and bifurcation theoretical analysis," Transportation Research Part B: Methodological, vol. 199, p. 103253, 2025. [Google Scholar] [Crossref]

10. K. E. ArunKumar, D. V. Kalaga, C. Mohan Sai Kumar, M. Kawaji, and T. M. Brenza, "Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends," Alexandria Engineering Journal, vol. 61, no. 10, pp. 7585-7603, 2022/10/01/ 2022, doi: https://doi.org/10.1016/j.aej.2022.01.011. [Google Scholar] [Crossref]

11. M. A. B. Siddique, B. Mahalder, M. M. Haque, and A. S. Ahammad, "Forecasting air temperature and rainfall in Mymensingh, Bangladesh with ARIMA: Implications for aquaculture management," Egyptian Journal of Aquatic Research, 2025. [Google Scholar] [Crossref]

12. J. Li, X. Xu, H. R. Karimi, and Z. Wu, "Optimization of electric vehicle charging strategies in residential integrated energy systems: A SARIMA model approach for dynamic electricity prices," Energy, p. 136600, 2025. [Google Scholar] [Crossref]

13. W. Feng et al., "Platelet demand forecasting based on the SARIMA model: optimizing blood bank resource allocation and clinical supply," Transfusion Clinique et Biologique, vol. 32, no. 2, pp. 185-194, 2025. [Google Scholar] [Crossref]

14. M. E. Elshaar and N. A. Qasem, "Enhanced Prediction of Airfoil’s Drag Coefficient using Curve Fitting and Artificial Neural Network," Transportation Research Procedia, vol. 84, pp. 641-648, 2025. [Google Scholar] [Crossref]

15. J. McKenzie, "Mean absolute percentage error and bias in economic forecasting," Economics Letters, vol. 113, no. 3, pp. 259-262, 2011. [Google Scholar] [Crossref]

• Applied Partial Differential Equation Modeling of Grating-Based Devices for Enhanced Sensing and Monitoring in the Petroleum and Gas Industry
• Analysis of Critical Path Method (CPM) To Optimized Project Scheduling
• Boussinesq Navier Stokes to A Modification on the Lorenz-63 Model