Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS
Abstract
In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional models, this study demonstrates the superior predictive capabilities of neural approaches, particularly in handling the non-linear dynamics and complex patterns inherent in financial time series data. The results indicate that N-HiTS and N-BEATS not only enhance the accuracy of forecasts but also boost the robustness and adaptability of financial predictions, offering substantial advantages in environments that require real-time decision-making. The paper concludes with insights into the practical implications of neural forecasting in financial markets and recommendations for future research directions.
Cite
@article{arxiv.2409.00480,
title = {Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS},
author = {Mohit Apte and Yashodhara Haribhakta},
journal= {arXiv preprint arXiv:2409.00480},
year = {2024}
}
Comments
This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution will be published in the Proceedings of the International Conference on Information Systems for Business Management (ISBM 2024), Springer Lecture Notes in Networks and Systems (LNNS), 2024