Machine Learning for Time Series Forecasting with Python

Machine Learning for Time Series Forecasting with Python

by Francesca Lazzeri
Released December 2020
Publisher(s): Wiley
ISBN: 9781119682363

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Book description

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. 

 

 

Table of contents

  1. Cover
  2. Title Page
  3. Introduction
    1. What Does This Book Cover?
    2. Reader Support for This Book
  4. CHAPTER 1: Overview of Time Series Forecasting
    1. Flavors of Machine Learning for Time Series Forecasting
    2. Supervised Learning for Time Series Forecasting
    3. Python for Time Series Forecasting
    4. Experimental Setup for Time Series Forecasting
    5. Conclusion
  5. CHAPTER 2: How to Design an End-to-End Time Series Forecasting Solution on the Cloud
    1. Time Series Forecasting Template
    2. An Overview of Demand Forecasting Modeling Techniques
    3. Use Case: Demand Forecasting
    4. Conclusion
  6. CHAPTER 3: Time Series Data Preparation
    1. Python for Time Series Data
    2. Time Series Exploration and Understanding
    3. Time Series Feature Engineering
    4. Conclusion
  7. CHAPTER 4: Introduction to Autoregressive and Automated Methods for Time Series Forecasting
    1. Autoregression
    2. Moving Average
    3. Autoregressive Moving Average
    4. Autoregressive Integrated Moving Average
    5. Automated Machine Learning
    6. Conclusion
  8. CHAPTER 5: Introduction to Neural Networks for Time Series Forecasting
    1. Reasons to Add Deep Learning to Your Time Series Toolkit
    2. Recurrent Neural Networks for Time Series Forecasting
    3. How to Develop GRUs and LSTMs for Time Series Forecasting
    4. Conclusion
  9. CHAPTER 6: Model Deployment for Time Series Forecasting
    1. Experimental Set Up and Introduction to Azure Machine Learning SDK for Python
    2. Machine Learning Model Deployment
    3. Solution Architecture for Time Series Forecasting with Deployment Examples
    4. Conclusion
  10. References
  11. Index
  12. Copyright
  13. About the Author
  14. About the Technical Editor
  15. Acknowledgments
  16. End User License Agreement

Product information

  • Title: Machine Learning for Time Series Forecasting with Python
  • Author(s): Francesca Lazzeri
  • Release date: December 2020
  • Publisher(s): Wiley
  • ISBN: 9781119682363