APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily
data-science machine-learning deep-learning time-series transformers neural-networks forecasting uncertainty-quantification time-series-analysis time-series-prediction time-series-forecasting conformal-prediction probabilistic-forecasting neural-ode temporal-convolutional-network pytorch-forecasting neural-ordinary-differential-equations
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Updated
Oct 28, 2025 - Python