Demand forecasting methods have been used in retail for a long time. Most of them are based on historical data, which is no longer useful in the new COVID-19 reality. If you used an ML-powered demand ...

An operational solar farm in Australia, where the study took place. Image: Nextracker. Machine learning techniques have been used in a study to boost the accuracy of renewables forecasts by up to 45%, ...

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based ...

In a new study led by the University of Washington, researchers have demonstrated artificial intelligence's ability to improve lightning forecasts. Lightning strikes led to the devastating California ...

Bank of America announced Friday the launch of CashPro Forecasting, an artificial intelligence (AI) and machine learning (ML) cash flow forecasting tool. It utilizes ML models based on a business's ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Miguel Jimenez receives funding from the National Aeronautics and Space Administration. With chatbots like ChatGPT making a splash, machine learning is playing an increasingly prominent role in our ...

Traditionally, CRM solutions have been unreliable for sales forecasting. Companies have dealt with inaccuracies and blamed their bad data. However, things are starting to change thanks to artificial ...

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

A new research paper shows the approach performs significantly better than the random-walk forecasting method.