Abstract: Data visualization aids in making data analysis more intuitive and in-depth, with widespread applications in fields such as biology, finance, and medicine. For massive and continuously ...

Code for our SIGKDD'25 paper: "BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models". The advent of universal time series forecasting models has revolutionized zero-shot ...

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Code ICML 2025 TimeFilter: Patch‑Specific Spatial‑Temporal Graph Filtration for Time Series Forecasting Code ICML 2025 K² ...

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 ...

In her new book, “Toni at Random,” Dana A. Williams highlights the groundbreaking writer’s time working in publishing. By Martha Southgate Martha Southgate is the author of four novels, most recently ...

Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...

Solargis’ Evaluate 2.0 platform uses more granular time series data. Image: Solargis. For years, the solar industry has relied on Typical Meteorological Year (TMY) data as the standard for PV ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

This is part of the “Crime and Safety Blind Spot” series, which presents an opportunity to understand various perspectives, entertain new ones, and consider different conclusions. Read the ...