Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

Abstract

Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management.