mtsa 0.2.6

Multiple Time Series Analysis

MTSA is a research toolkit designed to aggregate machine learning models for anomaly detection, with a strong focus on enhancing reproducibility and explainability in model implementation. It offers a structured environment for developing, testing, and comparing various anomaly detection approaches, prioritizing replicability and ease of use. The toolkit is continuously updated to include both classical and state-of-the-art algorithms for anomaly detection in multivariate time series.

🔧 Installation

To get started, clone the repository and install the required dependencies:

git clone https://github.com/your-username/MTSA.git
cd MTSA
pip install -r requirements.txt

🚀 Usage

MTSA allows you to run anomaly detection models on acoustic data collected from complex systems like industrial machines.

A complete example is available in the following Jupyter notebook:
👉 examples/MTSA.ipynb

Note: If you encounter issues while running on Google Colab, try upgrading the Colab package:

pip install --upgrade google-colab

🧠 Implemented Machine Learning Approaches

MTSA currently integrates the following anomaly detection models:

• Hitachi
  A robust autoencoder model specifically designed for industrial anomaly detection tasks.

• RANSynCoders
  Ensemble of autoencoders with FFT, leveraging bootstrapping to perform robust anomaly inference.

• GANF
  A model that combines graph structures, recurrent neural networks (RNNs), and normalizing flows to perform anomaly inference.

• Isolation Forest
  A tree-based ensemble method that isolates anomalies.

• OSVM (One-Class SVM)
  A support vector-based approach for detecting outliers by modeling the distribution of normal data.

And more!

🌐 Learn More

For full documentation, examples, and additional resources, visit our official website.