Enhancing Sales Forecasting Accuracy Through Optimized Holt-Winters Exponential Smoothing with Modified Improved Particle Swarm Optimization

The Holt-Winters Exponential Smoothing method utilizes three smoothing parameters, namely alpha (α), beta (β), and gamma (γ), which have a significant impact on the accuracy of the forecasting process. One of the main challenges in the Holt-Winters Exponential Smoothing method is to find the best combination of the smoothing parameters, α, β, and γ, to achieve optimal forecasting accuracy. In this research, the MIPSO optimization method is used to find the optimal combination of values for α, β, and γ. The sales data used in the study covers the period from January 2021 to May 2023. The research results indicate the best accuracy achieved by combining the Holt-Winters Exponential Smoothing algorithm with the MIPSO optimization algorithm during the data period from January 2021 to May 2023, with a MAPE value of 9.1717%. Therefore, the use of the MIPSO algorithm helps discover the optimal combination of α, β, and γ parameters for forecasting.