Search Results for author:
Found 2 papers, 0 papers with code
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting
We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability.
Optimizing timetable and network reopen plans for public transportation networks during a COVID19-like pandemic
We model the transit system as a space-time network.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
