In this tutorial, we build an advanced agentic AI system that autonomously handles time series forecasting using the Darts library combined with a lightweight HuggingFace model for reasoning. We ...

Abstract: The rise of decentralized energy sources and renewables demands advanced grid planning, with short-term load forecasting (STLF) playing a crucial role. Energy demand in smart grids is highly ...

A comprehensive machine learning-powered inventory forecasting system for POWERGRID Corporation, designed to predict material requirements, optimize procurement planning, and prevent stockouts through ...

Google Research introduces in-context fine-tuning (ICF) for time-series forecasting named as ‘TimesFM-ICF): a continued-pretraining recipe that teaches TimesFM to exploit multiple related series ...

Reactions to Kimmel's suspension, Trump publicly rebukes Putin, and more Length: Long Speed: 1.0x Every three months, participants in the Metaculus forecasting cup try to predict the future for a ...

Temperature impacts every part of the world. Meteorological analysis and weather forecasting play a crucial role in sustainable development by helping reduce the damage caused by extreme weather ...

Until recently, using machine learning for a specific task meant training the system on vast amounts of relevant data. The same was true for data representing a system that changes over time, says SFI ...

Malnutrition is a leading cause of morbidity and mortality for children under-5 globally. Low- and middle-income countries, such as Kenya, bear the greatest burden of malnutrition. The Kenyan ...

Abstract: Time series forecasting is a cornerstone of predictive analytics in diverse domains, such as electricity power production (power plants) and transmission (power grids). Predicting ...