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TimeNet

TimeNet is a Python library and CLI for registering, fetching, and exploring time-series datasets in a shared format called TimeF. It gives every dataset one on-disk shape and one way to load it, so a consumer reads ECGs, accelerometer traces, and market series through the same API.

Scope

TimeNet is not a modeling toolkit. Training, inference, model definitions, and evaluation metrics are out of scope. It stops at handing you the data.

How it fits together

TimeNet architecture diagram

A connector turns a raw source into a manifest plus parquet and publishes it to a registry. The client reads the manifest from the registry and loads the data. The client never runs connector code.

  • BaseConnector is the only contract a new data source must satisfy.
  • TimeFDataset is the in-memory model a connector populates during convert().
  • TimeFWriter serializes a populated TimeFDataset to disk.
  • TimeFReader reads a TimeF version directory back into a TimeFDataset.

Where next

  • Get started: install TimeNet and load your first dataset.
  • Architecture: how the packages, registries, and curation flow fit together.
  • Datasets: the datasets already curated into the registry.