Google Research released TimesFM 3.0 in late August 2026, a pretrained foundation model for zero-shot time series forecasting that requires no training, and the new version adds multivariate support. …
Skforecast-AI, a new Python package from the skforecast team, pairs a deterministic forecasting engine with an optional LLM reasoning layer to automate time series forecasting, returning both predicti…
Researchers from the University of California, Berkeley, Amazon, and other institutions introduced forking-sequences, a method that generates overlapping forecasts for every target date in a single fo…
A benchmark study of time series forecasting models reveals that simple statistical baselines and zero-shot foundation models outperform sophisticated neural and tree-based models on series with stron…
A new neuro-agentic control framework coupling an LLM-based planner with a pre-trained Time-Series Foundation Model (TimesFM) achieved zero physically invalid actions and prevented 33.3% of security b…
A developer released Zer0Fit, an open-source project that wraps Google's TabFM and TimesFM foundation models as a Model Context Protocol server, enabling LLM agents to invoke zero-shot ML tasks with n…
Google Research introduced TabFM, a foundation model for tabular data that performs zero-shot classification and regression without dataset-specific training. The model uses a hybrid-attention archite…
Researchers have developed GITCO, a gated inference-time context optimization framework that improves time series foundation model accuracy by selectively identifying and suppressing harmful data patc…