Abstract: We comprehensively compare thirteen machine learning models for forecasting urban air pollutants. However, the accuracy of existing prediction models varies as a function of what specific ...

Code for our SIGKDD'25 paper: "BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models". The advent of universal time series forecasting models has revolutionized zero-shot ...

Abstract: This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series ...