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TimeNet
Find, build, and load time-series datasets through one shared format.
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 Hackathon 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 ) to explore what it can do. Choose any use case, from forecasting and classification to explanation, reasoning and beyond. Register here: challenge.timenet.ai
Quick start
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:
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 for installation options and a longer example.
How it fits together
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 workspace with two packages:
Read the architecture guide for the complete design.
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 before redistribution.
About
Download and explore time-series datasets through one standardized format.