Time-Series Forecasting of Seasonal Items Sales Using Machine Learning

Authors: Yogeshwaran L, Dinesh Karthick K, Siddarth KM, Vivekanantha Rajeshwaran S

Abstract: Sales forecasting is an essential task in modern business environments, as it helps organizations make informed decisions related to inventory management, production planning, and resource allocation. Accurate prediction of sales becomes more challenging in the case of seasonal products, where demand varies significantly over time due to factors such as weather conditions, festivals, and consumer behaviour patterns. This paper presents a machine learning-based system for forecasting the sales of seasonal items using historical time-series data. The dataset is pre-processed by handling missing values, converting date formats, and organizing data in a structured manner. Feature engineering techniques extract meaningful temporal attributes such as month, day, and year, which play a crucial role in identifying seasonal patterns. A Linear Regression model analyses the relationship between the extracted features and sales values, achieving an R² score of 0.89 with low error metrics (MAE: 10.5, RMSE: 13.4). The system is deployed as a Flask-based web application enabling real-time sales predictions through a user-friendly interface. Results demonstrate that the proposed approach effectively captures seasonal trends and provides reliable predictions suitable for retail, e-commerce, and inventory management applications.

DOI: http://doi.org/10.5281/zenodo.20449169