PriorTime series★ Featured
AR(2) Time Series
y_t = φ₁·y_{t-1} + φ₂·y_{t-2} + ε_t
✓PFN Studio Team
Real-world-shaped time series. Random stationary AR(2) coefficients per task; the PFN learns to forecast any well-behaved autoregressive series.
y_t = φ₁·y_{t-1} + φ₂·y_{t-2} + ε_t
Real-world-shaped time series. Random stationary AR(2) coefficients per task; the PFN learns to forecast any well-behaved autoregressive series.