---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---

# CleanTS Model Card

## Introduction

CleanTS is a competitive pre-trained baseline for time-series forecasting. It is designed to demonstrate the potential of **data-centric optimization** using a **standard encoder-only Transformer** architecture without any structural modifications.

## Key Technical Principles

### 1. Pure Architecture

 Zero modifications to the attention mechanism or model structure. CleanTS proves that high-quality data governance can significantly elevate the performance of vanilla architectures.

### 2. Systematic Data Governance

CleanTS adheres to a strict data-centric pipeline:

- **Zero Synthetic Data:** All training is performed exclusively on **authentic, real-world data**.
- **Publicly Sourced:** The training corpus consists entirely of **publicly available datasets**, ensuring transparency and accessibility.
- **Advanced Cleaning:** We achieve promising results solely through systematic data cleaning and preprocessing strategies.

### 3. Contamination Prevention

We strictly guarantee that **no data from the GiftEval test set，Fev-bench，Fev-leaderboard and LSTF** was involved in the training phase.

> [!NOTE] A **detailed technical report**, including our specific data cleaning methodologies, training configurations, and comprehensive ablation studies, will be released concurrently with the upcoming publication of the full model.