TimeTuner is a general visual analytics framework. which is designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. In our work, we instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants.

• Front-end
  • Vue.js
  • Element Plus
• Backend
  • Flask
  • Python

• Frontend:
  • Vue.js 3
  • npm
• Backend:
  • Python: version 3.8 or higher
  • SHAP: version 0.41.0
  • pandas: version 1.5.3
  • tensorflow: version 2.10.0
  • keras: version 2.10.0
  • scikit-learn: version 1.2.2
  • numpy: version 1.24.3
  • Flask: version 2.2.0

1. Enter the folder

   cd <your-project-path>/TimeTunerSystem/Frontend

2. Install NPM packages

   npm install

3. Run the Frontend

   npm run dev

1. Enter the folder

   cd <your-project-path>/TimeTunerSystem/Backend

2. Run the Backend

   set FLASK_APP=app.py
   flask run 
   // or
   python app.py

@article{hao2023timetuner,
  title={TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations},
  author={Hao, Jianing and Shi, Qing and Ye, Yilin and Zeng, Wei},
  journal={arXiv preprint arXiv:2307.09916},
  year={2023}
}