---
task_categories:
- time-series-forecasting
---

# TSCOMP Corpus

[Paper](https://huggingface.co/papers/2605.26562) | [GitHub](https://github.com/SUFE-AILAB/TSCOMP)

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.

## Sample Usage

The corpus is designed to be used for meta-learning. You can use the provided code in the GitHub repository to run experiments or extract features:

```bash
# Run meta learning experiments
python meta/run.py --mode simple --test_dataset ETTh2 --meta_model_type mlp

# Extract meta-features for datasets
python meta/meta_features/get_meta_features_LTF.py --meta_feature_type tabpfn

# Apply meta selection to new datasets
python meta/run_custom.py --new_dataset my_dataset --checkpoint_path <path> --new_dataset_path <csv_path> --scripts_root <scripts_dir>
```

## 📝 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}
}
```