Vol. 4, Issue 8, Part A (2018)

Ensemble learning, a paradigm that leverages the strengths of multiple base learners, has demonstrated remarkable success in various ML applications. In the context of sales forecasting, ensemble methods such as bagging, boosting, and stacking have emerged as promising tools for harnessing diverse models to achieve superior predictive performance. This paper systematically reviews and analyzes a plethora of studies and applications that have employed ensemble learning techniques to enhance the accuracy of sales forecasts.

The review begins by providing an overview of traditional forecasting methods and their limitations, setting the stage for the exploration of ensemble learning as a potential solution. A detailed examination of bagging algorithms, such as Random Forests, reveals their ability to mitigate overfitting and capture intricate relationships within sales data. Boosting techniques, including Ada Boost and Gradient Boosting, are evaluated for their capacity to sequentially improve model accuracy by focusing on previously misclassified instances. Additionally, the paper delves into the intricacies of stacking, a meta-learning approach that combines the outputs of diverse base models to achieve a more robust and accurate ensemble prediction.

The critical analysis incorporates discussions on the strengths, weaknesses, and practical considerations of ensemble learning in the sales forecasting domain. Furthermore, the review addresses challenges related to data quality, feature selection, and model interpretability, offering insights into potential areas for future research and development.