This repository contains the code and examples for blog articles published on nixtla.io.

This project uses uv for Python package management. To set up the environment:

1. Install uv if you haven't already:

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

2. Create and activate a virtual environment:

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

3. Install dependencies:

uv sync pre-commit install

• notebooks/: Contains Jupyter notebooks for each blog post
• README.md: This file

Each blog post's code is organized in its own notebook, named according to the blog post title.

• Anomaly Detection (anomaly_detection.ipynb) - Learn how to detect anomalies in time series data using TimeGPT
• Baseline Forecasts (baseline_forecasts.ipynb) - Explore baseline forecasting methods and their effectiveness
• Intermittent Forecasting (intermittent_forecasting.ipynb) - Explore demand forecasting techniques for intermittent time series

The notebooks are standard Jupyter notebooks that can be run in any Jupyter environment.

To run the notebooks locally, you can use:

# Start Jupyter Lab uv run jupyter lab # Or start Jupyter Notebook  uv run jupyter notebook

This will start a local server where you can interact with the notebooks in your browser.

We welcome contributions to improve the code examples and documentation. Please see CONTRIBUTION.md for detailed guidelines on:

• Style and structure of blog posts
• Development workflow