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README.md

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-license: apache-2.0
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+# 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:
+
+```bibtex
+@inproceedings{liang2025beyond,
+  title={Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting},
+  author={Liang, Shuang and Hou, Chaochuan and Yao, Xu and Wang, Shiping and Huang, Hailiang and Han, Songqiao and Jiang, Minqi},
+  booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
+  year={2025}
+}
+```