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Project

Machine Learning Modeling & Implementation

Time-series forecasting is a fundamental challenge in machine learning — requiring models that can capture both long-term dependencies and abrupt pattern shifts. This project explores deep learning architectures for sequential data, with a focus on Multi-Head Attention mechanisms for stock market prediction, alongside LSTM-based models for general time-series forecasting.

Multi-Head Attention for Financial Forecasting

The primary research contribution is an implementation and evaluation of Multi-Head Attention architectures for stock market prediction, published at ICAMIMIA 2025 (Scopus-indexed international conference). The model processes temporal financial data through multiple parallel attention heads, each learning to focus on different temporal patterns — from short-term volatility to long-term trends.

The architecture was benchmarked against traditional LSTM and GRU baselines, with ablation studies to optimize the number of attention heads, hidden dimensions, and dropout rates. Results demonstrated that attention-based models capture abrupt pattern shifts more effectively than recurrent architectures alone, particularly during high-volatility market periods.

Technical Approach

Implemented in Python using TensorFlow and PyTorch, with extensive hyperparameter optimization via Optuna. Feature engineering included technical indicators (moving averages, RSI, MACD) and macroeconomic features. Ensemble methods combining attention, LSTM, and gradient boosting models provided robustness across different market conditions.

Architecture

1. Feature Ingestion & Prep Historical Financial & Technical Indices RSI, MACD, Moving Averages & Macro Features Engineered Features 2. Neural Architecture TensorFlow & PyTorch Deep Learning LSTM Sequential Block + Multi-Head Attention Parallel Temporal Pattern Modeling Attention Weights 3. Optimization & Tuning Hyperparameter Optimization (Optuna) GPU Cluster Training with FP16 Mixed Precision MLflow Run Registry & Model Checkpoint Log Best Model Weights 4. Production Deployed Pipeline Automated Walk-Forward Forecasting Live Data Streaming & Real-Time Inference Retraining Triggers & Concept Drift Audit

Production Deployment & Impact

Data pipelines were built for automated feature extraction, walk-forward validation to prevent look-ahead bias, and model retraining triggers based on performance degradation detection. The final deployed models run in production data pipelines for automated forecasting.

The research was published at ICAMIMIA 2025, contributing to the literature on attention mechanisms for financial time-series. Beyond the paper, the models and pipelines have been deployed in production environments for automated forecasting, demonstrating that academic research can transition to real-world applications with proper engineering practices.

Stack
PythonTensorFlowPyTorchLSTMMulti-Head Attention
▸ ICAMIMIA 2025▸ 100+ ablation runs▸ GPU cluster training