Beyond the lakehouse: Fundamental's NEXUS bypasses manual ETL with a native foundation model for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a ...
IEEE

Wavelet-Enhanced Hybrid LSTM-XGBoost Model for Predicting Time Series Containing Unpredictable Events

Abstract: Accurate electricity consumption forecasting is essential for effective power management, especially in the presence of unpredictable events that disrupt typical consumption patterns. Using ...

Fundamental emerges from stealth with first major foundation model trained for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
CNET

I Asked AI to Predict the Weather. It's About as Reliable as a Groundhog

Just as different AI models can say different things based on the same prompt and information, different groundhogs can ...
GitHub

[PUT ICML 2025] Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting

Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting models during ...
GitHub

Toto - Time Series Optimized Transformer for Observability

Toto is a foundation model for multivariate time series forecasting with a focus on observability metrics. This model leverages innovative architectural designs to efficiently handle the ...
9to5Mac

iOS 26.2 added new AirDrop upgrade, here’s how it works

AirDrop has long been a highly popular iPhone feature, offering an easy way to share photos, files, and more with friends and family. But in iOS 26.2, there’s a new AirDrop enhancement available: ...
IEEE

SparseTSF: Lightweight and Robust Time Series Forecasting via Sparse Modeling

Abstract: This paper introduces SparseTSF, a novel and extremely lightweight method for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal ...