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 ...

One can’t read any news today without a barrage of articles about data science and machine learning and artificial intelligence. Just recently, Jeff Bezos opened up his private MARS (Machine Learning, ...

Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...

Researchers in China have applied a machine learning technology based on temporal convolutional networks in PV power forecasting for the first time. The new model reportedly outperforms similar models ...

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%, ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

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 ...

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 ...