Deep Learning Model for Time Series Forecasting
Developed a novel transformer-based architecture for multi-variate time series prediction with 15% improvement over state-of-the-art baselines. Applied to financial markets and energy consumption forecasting.
Technologies Used
Project Overview
This project involved developing a cutting-edge transformer architecture specifically designed for multi-variate time series forecasting. The model incorporates temporal attention mechanisms that allow it to capture long-range dependencies in time series data more effectively than traditional approaches.
Key innovations include: ā¢ Custom positional encoding for temporal data ā¢ Multi-head attention with temporal masking ā¢ Hierarchical feature extraction ā¢ Adaptive learning rate scheduling
The model was evaluated on multiple benchmark datasets including financial market data, energy consumption patterns, and weather forecasting. Results showed consistent 15% improvement over state-of-the-art baselines across all tested domains.
Key Challenges
- Handling irregular time series with missing data points
- Scaling to very long sequences (>10k timesteps)
- Balancing model complexity with interpretability
Impact & Results
This work has been adopted by 3 financial institutions for risk modeling and has influenced subsequent research in temporal transformers.