Machine Learning applied to Projected Financial Statements (PFS)
DOI:
https://doi.org/10.18687/LACCEI2023.1.1.1420Keywords:
Artificial intelligence, projected financial statements, machine learning, scikit learn.Abstract
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.Downloads
Published
2023-07-27
Issue
Section
Articles
Copyright
Copyright (c) 2023 LACCEI
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
LACCEI retains copyright of all published articles under the terms of its copyright transfer agreement. As the copyright holder, LACCEI distributes the articles to the public under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
How to Cite
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