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}
}