Current frontier LLMs show incredible results across text, video, and audio, but don't do as well with real-world scenarios like production lines, trading, drilling operations, or demand supply forecasting. They are not capable of comprehending this data, especially given a limited context.

Models exist that are able to understand temporal information natively—Temporal Foundation Models (TFMs). However, there is a data problem: unlike all the public information that frontier models train on, temporal information is still scattered across organizations without a unified way to access it.

That’s why we built TimeNet. It's like ImageNet for temporal data:

- One uniform data layer for either our repository or your own data

- Define tasks on top temporal data around classification, question answering, scalar prediction, temporal localization, forecasting, generation, editing, and correspondence (more to come)

- Simple and performant API to integrate into ML frameworks.

- First experimental results: training on multiple temporal datasets increased F1 performance on each dataset respectively by an average of 14% and 22% for OpenTSLM and SLIP respectively.

- Install via ‘uv add timenet’

TimeNet is the result of a growing open and global collaboration between researchers from Aionic Labs, Stanford, ETH Zurich, UNSW Sydney, Sun Yat-sen University, Griffith University, Google, UCLA, TimeCopilot, Dartmouth, Harvard, Cambridge, Eindhoven University of Technology and KAIST.

Paper: https://arxiv.org/abs/2610.04407

Quickstart: https://docs.timenet.ai/get-started.html

TimeNet Registry: https://registry.timenet.ai/

There is currently an online hackathon ongoing on TimeNet (https://challenge.timenet.ai/) with up to $50k in cloud credit prizes, this should fuel your first model training. We would love to see you build on top of it and to hear your thoughts!