Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict the Future

by Jason Brownlee

Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

367 pages, ebook

Published January 1, 2017

About the author

Jason Brownlee, Ph.D. trained and worked as a research scientist and software engineer for many years (e.g. enterprise, R&D, and scientific computing), and is known online for his work on Computational Intelligence (e.g. Clever Algorithms), Machine Learning and Deep Learning (e.g. Machine Learning Mastery, sold in 2021) and Python Concurrency (e.g. Super Fast Python).

Jason writes fiction under the pseudonym J.D. Brownlee: https://www.goodreads.com/jdbrownlee

Reviews

[Mzakaria · Rating 4 out of 5]

This book teaches you how to study time series data using Python. And how to model it. It teaches the topic at hand well. There is no fear from over-simplification, repeating the point in many different places, and driving the lessons in this book using multiple, detailed examples.

This makes the book ideal for someone seeking to learn how to do this task using python with minimal knowledge in Python or in statistics.

The code provided in the book is well written. And it was easy to follow with the steps.

[Xingda Wang · Rating 5 out of 5]

Well structured and documented.