Haonan Chen, a CSU associate professor of electrical and computer engineering, and his team have published research that ...

Subseasonal forecasting, or the ability to predict weather trends two weeks to two months in advance, is a capability highly ...

Among amateur traders, prediction markets have taken off. Everyone from students to delivery drivers seems to be having a ...

In a previous article, we explored how BBVA is scaling its Machine Learning capabilities with a new architecture based on AWS ...

Kalshi launched a new online hub Wednesday for midterm election forecasting, displaying prediction market odds for major ...

Years of snake-tracking data reveal the conditions that boost success and that could be incorporated into an app to aid in ...

This problem, even more than insider trading, may start to limit what Polymarket and Kalshi can offer. There are plenty of ...

Caleb Davies, 46, is an unusual breed of prediction market trader whose homemade computer tools forecast how a movie or song ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

@misc{chandak2026scalingopenendedreasoningpredict, title={Scaling Open-Ended Reasoning to Predict the Future}, author={Nikhil Chandak and Shashwat Goel and Ameya ...

Abstract: In the competitive restaurant industry, success hinges on accurate demand forecasting and optimal pricing strategies. Weather factors such as temperature, rain, and seasonal changes ...

This repository contains a 7-lesson FREE course to teach you how to build a production-ready ML batch system. Its primary focus is to engineer a scalable system using MLOps good practices. You will ...