Machine Learning for Time Series Forecasting with Python - by Francesca Lazzeri (Paperback)

Book Synopsis

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource

Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling.

Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.

Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to:

• Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality
• Prepare time series data for modeling
• Evaluate time series forecasting models' performance and accuracy
• Understand when to use neural networks instead of traditional time series models in time series forecasting

Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.

Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

From the Back Cover

Discover hands-on techniques to build robust business forecasting models

Machine Learning for Time Series Forecasting with Python shows readers how to implement accurate and practical time series forecasting models using the Python programming language. Accomplished economist, data scientist, and author Francesca Lazzeri walks you through the foundational and advanced steps necessary to create successful forecasting applications.

Highly useful in industries as varied as finance, education, and health care, time series forecasting plays a major role in decision-making for businesspeople of all sorts. This book demystifies the technique, providing readers with little or no time series or machine learning experience the fundamental tools required to create and evaluate time series models.

Machine Learning for Time Series Forecasting with Python uses popular and common Python tools and libraries to accelerate your ability to solve complex and important business forecasting problems. You'll learn how to clean and ingest data, design end-to-end time series forecasting solutions, understand some classical methods for time series forecasting, incorporate neural networks into your forecasting models, and how to deploy your time series forecasting models for use in the real world.

Perfect for business analysts with two to three years of experience, developers, and data scientists, this book also belongs on the shelves of researchers familiar with time series forecast theoretical concepts but lacking in hands-on experience.

Written in a practical and accessible style, Machine Learning for Time Series Forecasting with Python teaches you:

• Time series forecasting concepts like horizon, frequency, trend, and seasonality
• How to evaluate the performance and accuracy of time series forecasting models
• When to use neural networks instead of traditional time series models in a forecasting application
• How to explore time series data, transform it, and use it to develop time series forecasting models
• How to use popular Python tools and packages like Jupyter notebooks, Scikit-learn, Keras, and TensorFlow

About the Author

FRANCESCA LAZZERI is an accomplished economist who works with machine learning, artificial intelligence, and applied econometrics. She works at Microsoft as a data scientist and machine learning scientist to develop a portfolio of machine learning services. She is a sought-after speaker and has given popular talks at AI conferences and academic seminars at Berkeley, Harvard, and MIT.