TimeNet is an open-source data infrastructure project intended to make time-series datasets easier to discover, prepare, share, and load across different tools. Its central proposal is TimeF, a common format that addresses the incompatible layouts used by existing datasets. TimeNet focuses on the data layer rather than model training, inference, or evaluation, which remain the responsibility of a user’s chosen framework. The project is explicitly pre-release, so its public API and format may change before version 1.0.
A TimeNet connector converts a raw dataset into an immutable TimeF version containing a manifest that identifies the dataset and its files, a DuckDB database describing its hierarchy and relationships, and a Parquet or Zarr store for signal values. The client can read a registered version without needing the connector that built it; it checks a local registry first, enabling offline access to locally built datasets. The Python workspace separates the core package - format, readers, writers, registries, SDK, and consumer CLI - from connectors and the dataset-building CLI. The quick-start example requires Python 3.11 or newer. TimeNet’s code is MIT-licensed, but datasets retain their own licenses and access conditions, so using the infrastructure does not grant permission to redistribute the data.
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