Ivan Shkvir

Improving long-horizon forecasting of neural networks via spectral features and decomposition

Long-horizon time series forecasting is commonly improved through increasingly specialized neural architectures, making the resulting advances difficult to transfer across forecasting models. We instead propose an architecture-independent representation that combines component-wise forecasting with selective spectral a...