SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
Better forecasts of lifetime earnings for government economic planning
A new AI model called SAGA predicts how much money people will earn over their entire working lives far more accurately than the methods used by finance ministries and central banks today. Tested on Swedish tax records spanning three decades and over 2 million people, it cuts prediction errors by nearly 38 percent at the twenty-year mark and produces reliable confidence intervals around its forecasts.
Governments use lifetime earnings predictions to design pension systems, tax policy, and welfare programs. Current methods miss real patterns in how earnings actually change over time, leading to inaccurate estimates of inequality and insufficient planning for retirement security. SAGA's 31–38 percent improvement in accuracy could help policymakers better anticipate future costs and design fairer systems—and the researchers released their model publicly so other governments can test it on their own data.