Time Series Analysis with Python Cookbook, Second Edition

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Repository Structure

├── code/           # Jupyter notebooks for each chapter
│   ├── Ch0/        # Setting up your Python environment (GitHub only)
│   ├── Ch1-Ch14/   # Main book chapters
│   ├── Bonus_Ch15/ # Probabilistic Forecasting (GitHub only)
│   └── Bonus_Ch16/ # Frequency Domain Analysis (GitHub only)
├── datasets/       # Data files used in recipes
└── README.md

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Getting Started

Each chapter folder contains its own environment configuration. Choose one of the following methods to set up your environment.

Option 1: Using uv (Recommended)

uv is a fast Python package manager that handles both virtual environments and dependencies. Each chapter includes:

• pyproject.toml: Defines the project metadata and dependencies
• uv.lock: Lock file ensuring reproducible installs

Setup steps:

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Navigate to a chapter folder
cd code/Ch1

# Create environment and install all dependencies
uv sync

# Activate the environment
source .venv/bin/activate  # macOS/Linux
# or
.venv\Scripts\activate     # Windows

# Launch JupyterLab
jupyter lab

Adding new packages:

uv add pandas matplotlib  # Adds to pyproject.toml and installs

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Option 2: Using pip and venv

Each chapter folder includes a requirements.txt file for pip-based installation.

# Navigate to a chapter folder
cd code/Ch1

# Create a virtual environment
python -m venv .venv

# Activate the environment
source .venv/bin/activate  # macOS/Linux
# or
.venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Launch JupyterLab
pip install jupyterlab
jupyter lab

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Option 3: Using conda

You can also use Conda to create an environment from the requirements file.

# Navigate to a chapter folder
cd code/Ch1

# Create a new conda environment with Python 3.12
conda create -n ch1 python=3.12 -y

# Activate the environment
conda activate ch1

# Install dependencies from requirements.txt
pip install -r requirements.txt

# Launch JupyterLab
conda install -c conda-forge jupyterlab -y
jupyter lab

Note: Some chapters also include an environment.yml file for direct conda environment creation:

conda env create -f environment.yml
conda activate <env-name>

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Chapter Overview

Chapter	Title	Bonus Recipe (GitHub)
Ch0	Setting Up Your Python Environment (GitHub only)
Ch1	Reading Time Series Data from Files	1 Recipe
Ch2	Reading Time Series Data from Databases	1 Recipe
Ch3	Persisting Time Series Data to Files	1 Recipe
Ch4	Persisting Time Series Data to Databases	1 Recipe
Ch5	Working with Date and Time in Python
Ch6	Handling Missing Data
Ch7	Outlier Detection Using Statistical Methods
Ch8	Exploratory Data Analysis and Diagnosis	2 Recipes
Ch9	Building Univariate Time Series Models Using Statistical Methods
Ch10	Additional Statistical Modeling Techniques for Time Series
Ch11	Forecasting Using Supervised Machine Learning
Ch12	Deep Learning for Time Series Forecasting
Ch13	Outlier Detection Using Unsupervised Machine Learning
Ch14	Advanced Techniques for Complex Time Series
Bonus Ch15	Probabilistic Forecasting (GitHub only)
Bonus Ch16	Analyzing Time Series in the Frequency Domain (GitHub only)

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Requirements

• Python 3.10 or higher (3.12 recommended)
• See individual chapter requirements.txt or pyproject.toml for specific dependencies

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