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Please install [Sintel](https://github.com/sintel-dev/sintel) first if you want to try the full feature of MTV.\n-   **Node.js (\u003e= 10.0.0)** - [Download \u0026 Install Node.js](https://nodejs.org/en/download/) and the npm package manager. Make sure to install gulp-cli globally after the installation of Node.js.\n\n## Get Started\n\n### Install\n\nDownload the repository\n\n```bash\n$ git clone https://github.com/sintel-dev/MTV mtv\n```\n\nOnce 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\n\n```bash\n$ npm install\n```\nTo avoid version conflicts and dependency issues, we have locked the versions of all packages and their dependencies in `package-lock.json`. The execution of the `npm install` command will, by default, install all packages using exactly the same versions specified in the `package-lock.json`.\n\n### Running Your Application\n\n#### 1. Run Sintel as the backend\nPlease 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.\n\n#### 2. Build MTV\n```bash\n$ npm run build\n```\n\n#### 3. Launch it\n```bash\n$ npm run serve\n```\n\nYour application should run on **port 4200** with the **_production_** environment by default. Just go to [http://localhost:4200](http://localhost:4200) in your browser (Chrome recommended).\n\n### Development\nIf you want to make changes on the interface and customize it to your own application scenario, you can run the following command:\n\n```bash\n$ npm start\n```\n\nEverytime you make changes on the source code, the interface will be automatically refreshed.\n\n## Citation\n```bib\n@article{10.1145/3512950,\n  author = {Liu, Dongyu and Alnegheimish, Sarah and Zytek, Alexandra and Veeramachaneni, Kalyan},\n  title = {MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series},\n  year = {2022},\n  issue_date = {April 2022},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  volume = {6},\n  number = {CSCW1},\n  url = {https://doi.org/10.1145/3512950},\n  doi = {10.1145/3512950},\n  journal = {Proc. ACM Hum.-Comput. Interact.},\n  month = {apr},\n  articleno = {103},\n  numpages = {30},\n  keywords = {anomaly detection, human-AI collaboration, collaborative analysis, visual analytics, time series, annotation}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsintel-dev%2Fmtv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsintel-dev%2Fmtv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsintel-dev%2Fmtv/lists"}