Recent advances in forecasting demand within emergency departments (EDs) have been bolstered by the integration of machine learning and time series analytical techniques. The objective of these ...

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 ...

In the wake of the disruptive debut of DeepSeek-R1, reasoning models have been all the rage so far in 2025. IBM is now joining the party, with the debut today of its Granite 3.2 large language model ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall accuracy across long time series. This design inherently favors common, low ...

The Covid-19 pandemic has proved that history no longer repeats itself when it comes to understanding consumer behavior. Demand forecasting systems have been ill-equipped to address disruptions to our ...

A range of national meteorological services across Europe and ECMWF have launched Anemoi, a framework for creating machine learning (ML) weather forecasting systems. Named after the Greek gods of the ...

Time series graphs are intuitive, helping you relate a metric to time. Marketing analysts are often faced with choosing a data visualization that speaks to managers and colleagues interested in ...

One of the unwritten axioms of data scientists specializing in machine learning methodologies is that they all try their hand at predicting the stock market. Some of the best attempts have turned a ...