Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS

TL;DR

This paper compares neural network models N-HiTS and N-BEATS for financial forecasting, demonstrating their superior accuracy and robustness over traditional models in predicting complex market trends.

Contribution

It provides a systematic comparison showing neural models outperform conventional methods in financial time series prediction, highlighting their practical advantages.

Findings

N-HiTS and N-BEATS improve forecast accuracy

Neural models handle non-linear financial data effectively

Enhanced robustness and adaptability in predictions

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.