Time Series Forecasting in Python

Master Time Series Forecasting with Python

Time series data surrounds us—from daily sales figures to hourly energy consumption. In this focused, hands-on ebook from Manning Publications, Marco Peixeiro helps you move beyond guesswork and build reliable forecasts using modern Python tools.

A Practical Path Through the Forecasting Workflow

The book begins with the fundamentals: understanding time series, baseline models, and random walks. You’ll quickly move into statistical methods like moving average, autoregressive, ARMA, ARIMA, and seasonal models. Then the journey continues into deep learning, where you’ll work with LSTMs, CNNs, and multi-step forecasting. Finally, you’ll learn to automate forecasting at scale with Prophet.

Real-World Capstone Projects

Each major section includes a capstone that ties everything together. You’ll forecast antidiabetic drug prescriptions, model household electric power consumption, and predict the monthly average retail price of steak in Canada. These projects give you the confidence to tackle your own forecasting challenges.

Who Will Benefit Most

This ebook is ideal for data scientists, analysts, and developers who already know basic Python and want to add practical time series forecasting skills. The code examples are clear, and the explanations keep theory accessible without sacrificing depth.

Your Digital Advantage

As a Digital Delights ebook, you can start reading immediately, search the text, and keep the entire reference with you wherever you work.

If you’re ready to turn historical data into useful predictions, this is a solid, hands-on guide to add to your library.