| # TSCOMP Corpus |
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| 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. |
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| ## Overview |
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| 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. |
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| ## Purpose |
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| 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. |
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| ## Dataset Structure |
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| 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. |
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| ## Source |
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| This corpus is generated from the official TSCOMP project: |
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| **https://github.com/SUFE-AILAB/TSCOMP** |
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| For more details on the experimental framework and component taxonomy, please refer to the associated paper. |
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| ## 📝 Citation |
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| If you find this work useful, please consider citing: |
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| ```bibtex |
| @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} |
| } |
| ``` |
