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 ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

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 ...

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...

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 ...

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 ...

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 ...

After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, two groups of companies emerged. One group has invested hundreds of ...