# 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{
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}
}
```