Metadata-Version: 2.1
Name: tsts
Version: 1.0.0
Summary: toolset for time series forecasting
Home-page: https://github.com/TakuyaShintate/tsts
Author: Takuya Shintate
Author-email: kmdbn2hs@gmail.com
License: MIT License
Keywords: tsts
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: yacs
Requires-Dist: numba
Requires-Dist: seaborn
Requires-Dist: terminaltables

<div align="center">
  <img src="img/tsts-logo.png" width="600"/>
</div>

---

[![pypi](https://img.shields.io/pypi/v/tsts?style=flat)](https://pypi.org/project/tsts/1.0.0/)
[![license](https://img.shields.io/github/license/TakuyaShintate/tsts?style=flat)](https://github.com/TakuyaShintate/tsts/blob/main/LICENSE)

([docs](https://takuyashintate.github.io/tsts/))

## 🏁 Introduction

tsts is an open-source easy-to-use toolset for **time series forecasting**.

## ✨ State of the Art

`tsts` provides many state-of-the-art models. See [here](https://takuyashintate.github.io/tsts/modules/models.html) for the full list of available models.

## 🔧 Installation

```
pip install tsts
```

## ⚡️ Gatting Started

- [Train on a Custom Dataset](https://takuyashintate.github.io/tsts/tutorials/train.html)
- [Test on a Custom Data](https://takuyashintate.github.io/tsts/tutorials/test.html)


