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The Use of Historic Data to Forecast Financial Revenue in an Electronics Company Using Machine Learning

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  • Thetshelesani Tshibubudze

    (Varsity College, Independent Institute of Education)

  • Lucas Thulani Khoza

    (Varsity College, The Independent Institute of Education)

Abstract

This research focuses on the importance of financial forecasting in organisational contexts, specifically in the electronics company sector. The integration of machine learning and artificial intelligence has greatly improved the accuracy and reliability of financial forecasting. This study uses advanced time series models like SARIMA, Exponential Smoothing, Holt-Winters Exponential Smoothing, and Long Short-Term Memory (LSTM) to analyse historical financial data and predict future revenue trends. The results show that LSTM outperforms the other models with an accuracy of 90%. Accurate financial forecasting is crucial for organisations to make informed decisions in a dynamic and competitive business environment. This study also explores the implications of inaccurate forecasting on the consumer electronics sector, emphasising the importance of accurate financial data for stakeholders like investors, creditors, managers, and regulators. The study concludes that accurate forecasting is essential for revenue prediction and expenditure reduction, and that the LSTM model offers organisations a valuable tool for financial planning and cost reduction.

Suggested Citation

  • Thetshelesani Tshibubudze & Lucas Thulani Khoza, 2025. "The Use of Historic Data to Forecast Financial Revenue in an Electronics Company Using Machine Learning," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-96-9697-0_58
    DOI: 10.1007/978-981-96-9697-0_58
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