An open source project from Data to AI Lab at MIT.

MTV

MTV is a visual analytics system built for anomaly analysis of multiple time-series data.

License

The MIT License

Prerequisites

Make sure you have installed all of the following prerequisites on your development machine:

• Sintel - MTV is the visual interface that requires running sintel as the backend. Please install Sintel first if you want to try the full feature of MTV.
• Node.js (>= 10.0.0) - Download & Install Node.js and the npm package manager. Make sure to install gulp-cli globally after the installation of Node.js.

Get Started

Install

Download the repository

$ git clone https://github.com/sintel-dev/MTV mtv

Once you've downloaded the MTV repository and installed all the prerequisites, you're just a few steps away from running your application. To install the project, create a virtualenv and execute

$ npm install

Running Your Application

1. Run Sintel as the backend

Please make sure Sintel runs on the port 3000. If not, you can change the config in the file src/model/utils/constants.tsx to ensure that MTV is able to connect to Sintel correctly.

2. Build MTV

$ npm run build

3. Launch it

$ npm run serve

Your application should run on port 4200 with the production environment by default. Just go to http://localhost:4200 in your browser (Chrome recommended).

Development

If you want to make changes on the interface and customize it to your own application scenario, you can run the following command:

$ npm start

Everytime you make changes on the source code, the interface will be automatically refreshed.

Citation

@article{10.1145/3512950,
  author = {Liu, Dongyu and Alnegheimish, Sarah and Zytek, Alexandra and Veeramachaneni, Kalyan},
  title = {MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series},
  year = {2022},
  issue_date = {April 2022},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {6},
  number = {CSCW1},
  url = {https://doi.org/10.1145/3512950},
  doi = {10.1145/3512950},
  journal = {Proc. ACM Hum.-Comput. Interact.},
  month = {apr},
  articleno = {103},
  numpages = {30},
  keywords = {anomaly detection, human-AI collaboration, collaborative analysis, visual analytics, time series, annotation}
}