Vol. 8, Special Issue 2 (2019)

The exploration begins by delineating the fundamental importance of accurate sales forecasting in modern business contexts, emphasizing its role in strategic decision-making and resource allocation. Subsequently, an in-depth analysis of the diverse feature selection methods commonly employed in the literature is presented. Techniques ranging from filter methods, wrapper methods, to embedded methods are scrutinized for their ability to enhance model efficiency by identifying and incorporating the most informative features.

A substantial portion of this review is devoted to elucidating the interplay between feature selection and the choice of machine learning algorithms. The examination encompasses popular algorithms such as linear regression, decision trees, support vector machines, and ensemble methods. The nuanced relationship between specific feature selection methods and the efficacy of each algorithm in the context of sales forecasting is thoroughly investigated.

Moreover, the review extends its purview to encompass real-world applications and case studies where the impact of feature selection on sales forecasting models is empirically validated. By synthesizing findings from diverse studies, the paper aims to distill overarching trends and patterns, shedding light on the generalizability of feature selection methods across different industry domains and dataset characteristics.