Resilience Forecasting through Advanced Predictive Models: A Transformer-Based Approach for Dynamic

2398 

Contributed Abstract 

Poster 

Fatemeh Salboukh (1), Hesam Saki (2), Lance Fiondella (1)

(1) N/A, N/A, (2) Department of Computer Engineering, University of Tehran, N/A

Hesam Saki  
Department of Computer Engineering, University of Tehran

Lance Fiondella  
N/A

Fatemeh Salboukh  
N/A

Fatemeh Salboukh  
N/A

Predicting long-term systems resilience is essential for strategic planning and risk management. Resilience forecasting plays a critical role in understanding and mitigating the impacts of shocks and facilitating quicker recoveries, especially in dynamic environments like crude oil markets. Traditional models lack dynamic adaptability. We proposed TimeGPT, a transformer-based model, to manage temporal dependencies and ensure system stability under stress. Using a 10-year crude oil price dataset, TimeGPT demonstrated robust zero-shot learning, enhanced by feature engineering (e.g., integrating external data like public holidays, anomaly indicators, and temporal trends) and fine-tuning. Attention mechanisms prioritized key features like US Rig Count, while filtering noise from less relevant variables such as Mortgage Rate and Import. Evaluated across different data splits (30%-70% to 70%-30%, incrementally by 10%), TimeGPT outperformed traditional models, capturing complex market dynamics and predicting long-term resilience. Metrics like MAE, RMSE, and R2 confirmed its accuracy. This approach supports strategic decision-making in uncertain economic environments.

Resilience prediction|time-varying covariates|Long-term prediction|Time series| TimeGPT models|

Section on Statistical Learning and Data Science

Machine Learning