Machine Learning applied to Projected Financial Statements (PFS)
DOI:
https://doi.org/10.18687/LACCEI2023.1.1.1420Palabras clave:
Artificial intelligence, projected financial statements, machine learning, scikit learn.Resumen
Projected financial statements represent one of the most reliable sources when it comes to making decisions involving the company's long-term performance. Therefore, finding methods to optimize their preparation and accuracy is the holy grail of financial accounting. The objective of this research is to use machine learning in projected financial statements, in order to obtain more accurate data through training in a tetradimensional space or also called Euclidean space of n dimensions.
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2023-07-27
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Derechos de autor 2023 LACCEI
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Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).
Cómo citar
Bellido-Zea, Coster, Villalobos-Meneses, Bertha, Alfaro Rodriguez, Carlos, Grados-Espinoza, Anna, Gomero-Ostos, Nestor, Hoyos-Rivas, Fernando, & Ramirez-Veliz, Francisco. (2023). Machine Learning applied to Projected Financial Statements (PFS). LACCEI, 1(8). https://doi.org/10.18687/LACCEI2023.1.1.1420