# UCTB

**Repository Path**: liweiowl/UCTB

## Basic Information

- **Project Name**: UCTB
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-26
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# UCTB (Urban Computing Tool Box)

 [![Python](https://img.shields.io/badge/python-3.6%7C3.7-blue)]() [![PyPI](https://img.shields.io/badge/pypi%20package-v0.3.0-sucess)](https://pypi.org/project/UCTB/) ![tensorflow](https://img.shields.io/badge/tensorflow-1.12%7C1.13-important)[![https://img.shields.io/badge/license-MIT-green](https://img.shields.io/badge/license-MIT-green)]() 

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**Urban Computing Tool Box** is a package providing **spatial-temporal prediction models** for various urban computing tasks, such as traffic prediction, crowd flow prediction, ridesharing demand prediction, etc. It contains both conventional models and state-of-art models. 

Currently the package supported the following models: ([Details](https://uctb.github.io/UCTB/md_file/static/current_supported_models.html))

- ARIMA
- DCRNN
- DeepST
- GeoMAN
- HM
- HMM
- ST-MGCN
- ST-ResNet
- STMeta
- XGBoost

UCTB is a flexible and open package. You can use the data we provided or use your own data, the data structure is well stated in the tutorial chapter. You can build your own model based on model-units we provided and use the model-training class to train the model.

[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://uctb.github.io/UCTB)