[Wiley] Machine Learning for Time Series Forecasting with Python

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Machine Learning for Time Series Forecasting with Python

• 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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关键词：Time Series Forecasting Learning Forecast machine

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Machine Learning for Time Series Forecasting with Python 向读者展示了如何使用 Python 编程语言实现准确实用的时间序列预测模型。成功的经济学家、数据科学家和作家 Francesca Lazzeri 将引导您完成创建成功预测应用程序所需的基础和高级步骤。

时间序列预测在金融、教育和医疗保健等各行各业中非常有用，在各种商人的决策中都发挥着重要作用。本书揭开了该技术的神秘面纱，为几乎没有或没有时间序列或机器学习经验的读者提供了创建和评估时间序列模型所需的基本工具。

使用 Python 进行时间序列预测的机器学习使用流行和常见的 Python 工具和库来加速您解决复杂和重要的业务预测问题的能力。您将学习如何清理和摄取数据、设计端到端时间序列预测解决方案、了解时间序列预测的一些经典方法、将神经网络纳入您的预测模型，以及如何部署您的时间序列预测模型以供使用在现实世界。

本书适合有两三年经验的业务分析师、开发人员和数据科学家，也属于熟悉时间序列预测理论概念但缺乏实践经验的研究人员的读物。

以实用且易于理解的风格编写，使用 Python 进行时间序列预测的机器学习：

时间序列预测概念，如范围、频率、趋势和季节性
如何评估时间序列预测模型的性能和准确性
何时在预测应用程序中使用神经网络而不是传统的时间序列模型
如何探索时间序列数据、对其进行转换并使用它来开发时间序列预测模型
如何使用流行的 Python 工具和软件包，如 Jupyter notebooks、Scikit-learn、Keras 和 TensorFlow

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