Machine Learning for Time-Series with Python: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Get better insights from time-series data and become proficient in building models with real-world data

Key Features

• Explore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks.
• Improve predictive modeling with feature engineering and forecasting machine learning techniques.
• Apply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.

Book Description

The Python ecosystem offers a wide range of tools for time series analysis and time series forecasting. Machine Learning for Time Series, Second Edition provides a practical guide to building forecasting systems while developing a solid understanding of modern predictive modeling techniques.

Starting with the fundamentals of time series data, you'll learn how to prepare datasets, perform feature engineering, and build forecasting pipelines. The book covers traditional methods such as ARIMA, SARIMA, and GARCH, alongside machine learning approaches including gradient boosting, recurrent neural networks, and deep learning models.

Through practical examples and clear explanations, you'll learn how to choose the right model for the right problem and improve forecasting accuracy across multiple applications. Updated content includes forecasting and signal extraction for financial markets, plus case studies from operations management, digital marketing, healthcare, and financial forecasting.

By the end of this book, you'll be able to confidently perform time series analysis and build effective forecasting systems using Python.

What you will learn

• Visualize time series data with ease
• Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
• Get to grips with classical time series models such as ARMA, ARIMA, and more
• Understand modern time series methods including the latest deep learning and gradient boosting methods
• Choose the right method to solve time-series problems
• Become familiar with libraries such as Prophet, sktime, statsmodels, XGBoost, and TensorFlow
• Understand both the advantages and disadvantages of common models
• Evaluate high-performance forecasting solutions

Who this book is for

This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

Table of Contents

1. Towards Modern Forecasting
2. Preparing and Visualizing Time Series Data
3. Classical Models and Validation
4. Forecasting with Machine Learning
5. Feature Engineering and Tree-Based Models
6. Multivariate and Hierarchical Forecasting
7. Practical Deep Learning for Time Series
8. Quantifying Time Series Uncertainty with Conformal Prediction
9. Foundation Models: Quantitative and Qualitative Forecasting
10. Production Workflows: Deployment, Monitoring, and Scaling
11. Beyond Forecasting: Specialized Applications
12. Intermittent Forecasting and Survival Analysis

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.