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data list | label int64 0 9 |
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UCR-2018
Parquet conversion of the 128 datasets in the official UCR 2018 time series classification archive.
Usage
Each original dataset is exposed as a separate Hugging Face config with its official train and test splits.
from datasets import load_dataset
dataset = load_dataset("ChengsenWang/UCR-2018", "ACSF1")
Use any directory name in this repository as the config name. Original label values and their integer encodings are recorded in label_mappings.json.
Dataset Structure
Each row contains:
data: afloat32time-series matrix with shape(L, C).label: a consecutiveint64class index.
UCR-2018/
├── README.md # Dataset card
├── statistics.csv # Dataset-level summary statistics
├── label_mappings.json # Original-to-integer label mappings
└── <dataset_name>/ # One Hugging Face config
├── train.parquet # Official training split
└── test.parquet # Official test split
Processing
- Preserve the official train/test split of every dataset.
- Timestamp-free series align by position, right-padded with NaN; timestamped series align on the dataset-level timestamp union.
- Parse all values as float32; missing, invalid, and non-finite values become NaN.
- Sort labels deterministically, then encode as consecutive integers from 0 to K-1.
- Write ZSTD-compressed Parquet files and verify by reading every value back.
Source
- Official archive: https://timeseriesclassification.com/aeon-toolkit/Archives/Univariate2018_ts.zip
- Archive website: https://timeseriesclassification.com/
Licensing and Citation
Licensing and citation requirements may differ between the individual datasets. Refer to the original archive and the source publication of each dataset before redistribution or use.
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