A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
KDSelector proposes a novel knowledge-enhanced and data-efficient framework for learning a neural network (NN)-based model selector in the context of time series anomaly detection (TSAD). It aims to address the limitations of existing model selection methods, which often fail to fully utilize the knowledge in historical data and are inefficient in terms of training speed.
We introduce a novel neural network (NN)-based selector learning framework, which serves as the core component of our system. For a comprehensive understanding of its architecture and implementation, please refer to the detailed technical report available at KDSelector Technical Report.
Look in SIGMOD2025 for details.
To install KDSelector from source, you will need the following tools:
gitconda(anaconda or miniconda)
The following key tools and their versions are used in this project:
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Python
- python==3.8.20
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Machine Learning and Deep Learning
- scikit-learn==1.3.2
- torch==1.13.
For the complete list of dependencies, please refer to the environment.yml and requirements.txt files.
Step 1: Clone this repository using git and change into its root directory.
git clone https://github.com/chenyuanTKCY/KDSelector.git
cd KDSelector/Step 2: Create and activate a conda environment named KDSelector.
conda env create --file environment.yml
conda activate KDSelectorNote: If you plan to use GPU acceleration, please ensure that you have CUDA installed. You can refer to the CUDA installation instructions for guidance.
If you do not wish to create the conda environment, you can install only the dependencies listed in requirements.txt using the following command:
pip install -r requirements.txt
Step 3: 👏 Installation complete! 👏
Start our system using the following command:
streamlit run app/Home.py