Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction

Probabilistic time series forecasting, essential in domains like healthcare and neuroscience, requires models capable of capturing uncertainty and intricate temporal dependencies. While deep learning has advanced forecasting, generating calibrated probability distributions over continuous future values remains challenging. We introduce Temporal Generative Flow Networks (Temporal GFNs), adapting Generative Flow Networks (GFNs) - a powerful framework for generating compositional objects - to this sequential prediction task. GFNs learn policies to construct objects (eg.