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

Music City Center 

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