Deep Time Series Models: A Comprehensive Survey and Benchmark
Paper • 2407.13278 • Published
Datasets:
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Handwriting (TsFile)
Apache TsFile version of the Handwriting UEA classification subset of
thuml/Time-Series-Library.
Overview
Accelerometer traces of writing the 26 letters; classify the letter.
- Train samples: 150.
- Test samples: 850.
- Dimensions (channels): 3.
- Series length: 152.
- Classes: 26.
Each sample is an independent multivariate series. TRAIN and TEST are stored as
two separate TsFiles (Handwriting_train.tsfile / Handwriting_test.tsfile).
Class labels are stored as separate CSV sidecar files in labels/.
Schema (TsFile structure)
- sample_index (TAG) - one device per sample. Query one sample with
WHERE sample_index=0. - Time (INT64) - within-sample position (0..151), a frame index rather than a wall-clock timestamp.
- dim_0..dim_2 (FIELD, FLOAT) - the 3 channels at each position.
- Labels (CSV sidecar) -
labels/Handwriting_train_labels.csvandlabels/Handwriting_test_labels.csvcontainsample_index,class_label, one row per sample.
Converted from the sktime .ts format. No dimensions, time points, samples, or
labels are dropped. Labels are not stored inside the TsFile feature tables.
Usage
Read the .tsfile files with the Apache TsFile Java or Python SDK. Join labels
from the sidecar CSV files by sample_index within the matching split.
Source & license
- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset
Handwriting) - Author / publisher: thuml (Tsinghua University)
- Paper: https://arxiv.org/abs/2407.13278
- License: CC BY 4.0
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