Business Forecasting Using Machine Learning Algorithms

  • Authors

    • Marco Bianchi Software Architect, Huawei, China. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2023PI1X7Q

    Published 06-04-2023

  • Business Forecasting, Machine Learning, Artificial Intelligence, Predictive Analytics, Sales Forecasting, Demand Prediction, Time Series Analysis, Random Forest, Support Vector Machine, Gradient Boosting, XGBoost, Artificial Neural Networks, Long Short-Term Memory (LSTM), Feature Engineering, Big Data Analytics, Business Intelligence, Decision Support Systems

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    Bianchi, M. (2023). Business Forecasting Using Machine Learning Algorithms. International Journal of Commerce, Finance and Digital Economy, 6(1), 01-19. https://doi.org/10.67228/3071642X/IJCFDE-2023PI1X7Q

  • Abstract

    Business forecasting plays a crucial role in helping organizations predict future trends and make informed decisions. Traditional forecasting methods often struggle with complex and rapidly changing business environments. Machine learning (ML) overcomes these limitations by learning patterns from historical and real-time data, improving prediction accuracy and adaptability. This study reviews ML-based forecasting techniques, covering data preprocessing, feature engineering, model selection, training, and performance evaluation using metrics such as MAE, RMSE, MAPE, and R². It also highlights applications in finance, retail, manufacturing, healthcare, logistics, and e-commerce. The findings indicate that ensemble and deep learning models outperform conventional statistical methods, offering scalable, intelligent, and data-driven forecasting solutions that enhance business performance and strategic decision-making.

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