Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior.

Techniques used to detect anomalies typically require training before being applied to new data.

This Jupyter Notebook reproduces the results from Oana Niculaescu's XRDS article: "Applying Data Science for Anomaly and Change Point Detection".

• Statistical anomaly detection methods
• Change point detection algorithms
• Data visualization with matplotlib
• Reproducible analysis with Jupyter notebooks

This project uses uv for Python package management.

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Create a virtual environment and install dependencies:

uv sync

Activate the virtual environment:

source .venv/bin/activate  # On Linux/macOS
# or
.venv\Scripts\activate  # On Windows

Start Jupyter Notebook:

uv run jupyter notebook

Or use Jupyter Lab for a modern interface:

uv run jupyter lab

Then open anomaly-detection.ipynb in the browser.

Export the notebook to HTML:

uv run jupyter nbconvert --execute --to html anomaly-detection.ipynb

Export to PDF (requires LaTeX):

uv run jupyter nbconvert --execute --to pdf anomaly-detection.ipynb

Export to LaTeX:

uv run jupyter nbconvert --execute --to latex anomaly-detection.ipynb

• Jupyter Notebook Documentation
• nbconvert Documentation
• uv Package Manager
• Original XRDS Article

See LICENSE for details.