Trend Extrapolation

Trend extrapolation in machine learning focuses on developing models capable of accurately predicting outcomes beyond the range of their training data. Current research emphasizes improving the extrapolation capabilities of various architectures, including neural networks (especially recurrent and implicit networks), and exploring algorithms like Gaussian processes and Bayesian methods to quantify prediction uncertainty. This research is crucial for advancing fields like materials science, weather forecasting, and quantum computing, where accurate predictions outside known data boundaries are essential for discovery and optimization.