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

Thetshelesani Tshibubudze () and Lucas Thulani Khoza ()
Additional contact information
Thetshelesani Tshibubudze: Varsity College, Independent Institute of Education
Lucas Thulani Khoza: Varsity College, The Independent Institute of Education

A chapter in LISS 2024, 2025, pp 739-766 from Springer

Abstract: 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.

Keywords: Financial forecasting; predictive models; machine learning; consumer electronics sector; historical data; financials revenues (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_58

Ordering information: This item can be ordered from
http://www.springer.com/9789819696970

DOI: 10.1007/978-981-96-9697-0_58

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().