Ready-to-use time series datasets for PyTorch Lightning
Project description
chronocratic-datasets
Ready-to-use time series datasets for PyTorch Lightning.
Installation
Install the package via pip:
pip install chronocratic-datasets
Note: The PyPI package name uses a hyphen (
chronocratic-datasets), but the import uses thechronocratic.datasetsnamespace:from chronocratic.datasets import ...
Quick Start
from pathlib import Path
from chronocratic.datasets import ForecastingMode, WeatherDataModule
weather = WeatherDataModule(
dataset_file_path=Path("data/weather.csv"),
mode=ForecastingMode.UNIVARIATE,
)
weather.prepare_data()
weather.setup()
train_loader = weather.train_dataloader()
Datasets
Forecasting
- ETT (Electricity Transformer Temperature): ETTh1, ETTh2, ETTm1, ETTm2 — transformer temperature data at hourly and 15-minute intervals
- Weather: Weather and meteorological features from 2012 to 2017
- Electricity: Hourly electricity load data
Classification
- UCR (Univariate): Archive of univariate time series classification datasets
- UEA (Multivariate): Archive of multivariate time series classification datasets
Features
- PyTorch Lightning DataModules — Drop-in
LightningDataModuleimplementations for seamless integration with Lightning training loops - Automatic caching with atomic writes — Downloaded and processed data is cached locally with atomic file operations to prevent corruption
- DDP-compliant data loading — Workers share cached data correctly under Distributed Data Parallel training
- Multiple forecasting modes — Switch between
UNIVARIATEandMULTIVARIATEforecasting configurations - Built-in scaling — MinMax and Standard scalers applied automatically per dataset conventions
- Type-safe API — Full type hints and Google-style docstrings for IDE autocomplete and static analysis
Documentation
Comprehensive documentation, including API reference, quickstart guides, and contributing instructions, is available at chronocratic-datasets.readthedocs.io.
License
BSD 3-Clause — see LICENSE for the full text.
Project details
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