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EMAT: Enhanced Multi-Aspect Attention Transformer for Financial Time Series Forecasting.

Yingjun Chen¹, Wenfeng Shen¹, Han Liu¹

¹School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China.

Entropy (Basel, Switzerland)

|October 28, 2025

Summary

This summary is machine-generated.

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This study introduces the Enhanced Multi-Aspect Transformer (EMAT) for stock market prediction, improving financial time series forecasting. EMAT effectively captures complex market dynamics, outperforming existing models in accuracy.

Area of Science:

Quantitative Finance
Machine Learning
Deep Learning

Background:

Financial time series prediction is challenging due to non-stationarity and complex dependencies.
Traditional methods struggle with multifaceted market dynamics like temporal proximity, trend, and volatility.

Purpose of the Study:

To propose a novel deep learning architecture, the Enhanced Multi-Aspect Transformer (EMAT), for stock market prediction.
To address limitations of existing models in capturing complex financial market characteristics.

Main Methods:

Developed EMAT, a deep learning model with a Multi-Aspect Attention Mechanism.
Incorporated an encoder-decoder structure with SwiGLU activation and a multi-objective loss function.
Evaluated EMAT on multiple stock market datasets against state-of-the-art baselines.

Keywords:

Multi-Aspect Attention Mechanism financial time series prediction stock market forecasting transformer model volatility-aware modeling

Related Experiment Videos

Main Results:

EMAT consistently outperformed various baseline models, including recurrent, hybrid, and Transformer architectures.
Ablation studies confirmed the critical contribution of each component within the Multi-Aspect Attention Mechanism.
Demonstrated significant improvements in predictive accuracy for financial forecasting.

Conclusions:

EMAT is an effective and robust tool for financial forecasting.
The model's ability to simultaneously model temporal decay, trend dynamics, and volatility regimes enhances predictive power.
EMAT offers substantial accuracy improvements over existing financial prediction approaches.