ProtoTS

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ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting

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# ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting

[![Paper](https://img.shields.io/badge/arXiv-2509.23159-B31B1B.svg)](https://arxiv.org/abs/2509.23159)
[![Conference](https://img.shields.io/badge/ICLR-2026-blue.svg)](https://iclr.cc/)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository contains the official implementation for the **ICLR 2026** paper: **"ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting"**.

---

## 📖 Overview

**ProtoTS** is a novel interpretable forecasting framework that achieves both high accuracy and transparent decision-making through modeling prototypical temporal patterns across hierarchical scales.

![Overview](https://raw.githubusercontent.com/SKURA502/ProtoTS/main/figs/overview.jpg)

## 🛠️ Installation

To set up the environment and install all dependencies, run:

```bash
pip install -r requirements.txt
```

## 🚀 Usage
To train the model or reproduce the experimental results, execute the provided training script:

```bash
bash scripts/train.sh
```

## ✍️ Citation
If you find our work or code useful in your research, please consider citing:

```Code Snippet
@article{peng2025protots,
  title={ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting},
  author={Peng, Ziheng and Ren, Shijie and Gu, Xinyue and Yang, Linxiao and Wang, Xiting and Sun, Liang},
  journal={arXiv preprint arXiv:2509.23159},
  year={2025}
}
```

ProtoTS

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