The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
TSCOMP Corpus
The TSCOMP (Time-Series Component-level Benchmarking) Corpus is a curated collection of evaluation results from systematic component-level experiments in deep multivariate time-series forecasting.
Overview
The corpus contains metrics.npy files from over 20,000 experimental runs, each recording the performance of a specific model component configuration (e.g., normalization layers, attention mechanisms, patching strategies) across multiple forecasting benchmarks.
Purpose
This corpus enables researchers to train custom meta predictors — models that learn to predict the best component configuration for a given time-series dataset, without needing to run expensive ablation studies themselves.
Dataset Structure
Each subdirectory in the archive corresponds to a downstream dataset (e.g., ECL, ETTh1, Exchange, weather). Within each dataset folder, individual experiment directories encode the full component configuration in their names, and metrics.npy contains the evaluation metrics for that run.
Source
This corpus is generated from the official TSCOMP project:
https://github.com/SUFE-AILAB/TSCOMP
For more details on the experimental framework and component taxonomy, please refer to the associated paper.
📝 Citation
If you find this work useful, please consider citing:
@inproceedings{
liang2026beyond,
title={Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting},
author={Shuang Liang and Chaochuan Hou and Xu Yao and Shiping wang and Hailiang Huang and Songqiao Han and Minqi Jiang},
booktitle={KDD 2026 Datasets and Benchmarks Track (Cycle 2)},
year={2026}
}
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