TimeNet: Open-source data infrastructure for Temporal Foundation Models

OpenTSLM released TimeNet, an MIT-licensed, pre-release open-source data infrastructure project that gives time-series datasets a single storage format, TimeF, and one API to find, build, download and load them. TimeNet requires Python 3.11 or newer and ships as two packages — `timenet` (the TimeF model, reader, writer, registries, SDK and consumer CLI) and `timenet-connectors` (dataset connectors and the `timenet-build` producer CLI) — with each TimeF version containing a `manifest.json`, a `control.duckdb` hierarchy store, and a Parquet or Zarr values plane. The project warns its public API and the TimeF format can change before 1.0, and notes its MIT license covers the code but not the datasets connectors fetch, which keep their upstream licenses and access terms.

Find, build, and load time-series datasets through one shared format. Note TimeNet is a pre-release project. Its public API and the TimeF format can change before 1.0. Time-series datasets use many incompatible layouts. TimeF gives them one storage format. TimeNet provides one API to find, build, download, and load them. TimeNet handles the data layer. Model training, inference, and evaluation stay in your chosen framework. TimeNet Online Hackathon is live. Pick a time-series problem, onboard a dataset in TimeNet format, then train or fine-tune a Temporal AI Model e.g. OpenTSLM https://github.com/OpenTSLM/OpenTSLM to explore what it can do. Choose any use case, from forecasting and classification to explanation, reasoning and beyond. Register here: challenge.timenet.ai https://challenge.timenet.ai/ TimeNet needs Python 3.11 or newer. uv add 'timenet cli ' uv tool install timenet-connectors timenet-build build timenet/hello-world The build lands in the local registry at ~/.timenet/registry/timenet/hello-world/1.0.0/ . Load it with Python: python from timenet import TimeNet dataset = TimeNet .load "timenet/hello-world@1.0.0" dataset.describe signal = dataset.records 0 .signals 0 values = signal.to numpy TimeNet reads the local registry first, and the local registry holds this exact version. So the example works without registry credentials or network access. See the Get started guide https://docs.timenet.ai/get-started.html for installation options and a longer example. A connector converts a raw source into a TimeF version. The version contains: - manifest.json , which identifies the dataset and lists its files - control.duckdb , which stores the hierarchy and relationships - a Parquet or Zarr values plane, which stores Signal values A registry stores immutable TimeF versions. The client reads a version without importing its connector. The repository is a uv https://docs.astral.sh/uv/ workspace with two packages: | Package | Purpose | |---|---| | timenet | The TimeF model, reader, writer, registries, SDK, and consumer CLI. | | timenet-connectors | Dataset connectors and the timenet-build producer CLI. | Read the architecture guide https://docs.timenet.ai/architecture.html for the complete design. TimeNet uses the MIT License https://github.com/OpenTSLM/TimeNet/blob/main/LICENSE . This license covers the TimeNet code, not the datasets that connectors fetch. Each dataset keeps its upstream license and access terms. Read the dataset licensing guide https://docs.timenet.ai/catalog/licensing.html before redistribution.