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Related Experiment Videos

Intelligent financial forecasting using transformers, neuro-symbolic AI, and agent-based systems.

V Jeyajeev1, R Jagadeesh Kannan1, R Deebalakshmi1

  • 1SRM Institute of Science and Technology Tiruchirappalli, Tiruchirappalli, 621105, Tamil Nadu, India.

Scientific Reports
|May 30, 2026
PubMed
Summary

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Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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This summary is machine-generated.

This study introduces an AI framework using a transformer model and LLM-based decision-making for accurate stock price prediction. It enhances financial forecasting with interpretable and adaptable AI-driven strategies.

Area of Science:

  • Artificial Intelligence
  • Financial Forecasting
  • Deep Learning

Background:

  • Stock market forecasting is challenging due to volatile prices and complex temporal dependencies.
  • Existing models struggle with unstable trends, leading to inaccurate predictions.
  • Need for adaptable and interpretable AI-driven financial forecasting models.

Purpose of the Study:

  • To develop an AI-driven framework for accurate and understandable stock price prediction.
  • To integrate a sequence-to-prediction transformer with LLM-based decision-making.
  • To enhance financial forecasting accuracy and interpretability.

Main Methods:

  • Utilized a transformer-enabled deep learning approach for price forecasting.
  • Incorporated multi-head attention mechanisms for trend identification.
Keywords:
AI-driven decision-makingAdvanced AI Agent ArchitectureNeuro-Symbolic AISequence-to-Prediction TransformerStock Market Price Prediction

Related Experiment Videos

  • Employed Neuro-Symbolic AI (NSAI) and Advanced AI Agent Architecture for decision-making and validation.

    • Main Results:

    • The AI framework demonstrates improved adaptability to market changes.
    • Combined deep learning with symbolic reasoning for accurate and interpretable predictions.
    • The Advanced AI Agent Architecture enhanced decision quality using LLM-based information.

    Conclusions:

    • The proposed AI framework offers a flexible and explainable approach to stock market forecasting.
    • This research advances financial market analysis through interpretable AI strategies.
    • The findings can lead to improved investment methods for traders and investors.