Forecasting Competition

Forecasting competitions aim to evaluate and improve predictive models by pitting them against each other on real-world datasets, focusing on both accuracy and the incentives driving model design. Current research emphasizes developing robust evaluation methods that account for multiple criteria and address incentive issues, such as hedging, that can arise in competitive settings. Prominent approaches include language models augmented with information retrieval and reasoning capabilities, as well as various time series models like LSTMs and those employing linear mappings. These advancements have implications for diverse fields, improving the accuracy and efficiency of forecasting in areas ranging from economics and policy to resource management and entertainment.