• 出版社 ‏ : ‎ Wiley; 第 1st 版 (2020年12月15日)
• 语言 ‏ : ‎ 英语
• 平装 ‏ : ‎ 224页
  

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
  

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