Practical Time Series Analysis

Practical Time Series Analysis pdf epub mobi txt 电子书 下载 2025

Aileen has worked in corporate law, physics research labs, and, most recently, a variety of NYC tech startups. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. Aileen is currently working at an early-stage NYC startup that has something to do with time series data and neural networks. She also serves as chair of the New York City Bar Association’s Science and Law committee, which focuses on how the latest developments in science and computing should be regulated and how such developments should inform existing legal practices.

In the recent past, Aileen worked at mobile health platform One Drop and on Hillary Clinton's presidential campaign. She is a frequent speaker at machine learning conferences on both technical and sociological subjects. She holds an A.B. from Princeton University and is A.B.D. in Applied Physics at Columbia University.

出版者:O'Reilly
作者:Aileen Nielsen
出品人:
页数:504
译者:
出版时间:2019-10-29
价格:USD 69.99
装帧:Paperback
isbn号码:9781492041658
丛书系列:
图书标签:
  • 机器学习 
  • 金融 
  • 算法 
  • 计算机 
  • 数据科学 
  • 数学和计算机 
  •  
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Solve the most common data engineering and analysis challenges for modern time series data. This book provides an accessible well-rounded introduction to time series in both R and Python that will have software engineers, data scientists, and researchers up and running quickly and competently to do time-related analysis in their field of interest.

Author Aileen Nielsen also offers practical guidance and use cases from the real world, ranging from healthcare and finance to scientific measurements and social science projections. This book offers a more varied and cutting-edge approach to time series than is available in existing books on this topic.

具体描述

读后感

评分

代码: [https://github.com/PracticalTimeSeriesAnalysis/BookRepo] 主要内容可见作者的SciPy 2019的讲座,看视频比较省事 [https://www.youtube.com/watch?v=v5ijNXvlC5A] (含slides与代码) 主要介绍了time series处理的各类方法 - 传统统计方法: ARIMA - State Model: HMM ...

评分

代码: [https://github.com/PracticalTimeSeriesAnalysis/BookRepo] 主要内容可见作者的SciPy 2019的讲座,看视频比较省事 [https://www.youtube.com/watch?v=v5ijNXvlC5A] (含slides与代码) 主要介绍了time series处理的各类方法 - 传统统计方法: ARIMA - State Model: HMM ...

评分

代码: [https://github.com/PracticalTimeSeriesAnalysis/BookRepo] 主要内容可见作者的SciPy 2019的讲座,看视频比较省事 [https://www.youtube.com/watch?v=v5ijNXvlC5A] (含slides与代码) 主要介绍了time series处理的各类方法 - 传统统计方法: ARIMA - State Model: HMM ...

评分

代码: [https://github.com/PracticalTimeSeriesAnalysis/BookRepo] 主要内容可见作者的SciPy 2019的讲座,看视频比较省事 [https://www.youtube.com/watch?v=v5ijNXvlC5A] (含slides与代码) 主要介绍了time series处理的各类方法 - 传统统计方法: ARIMA - State Model: HMM ...

评分

代码: [https://github.com/PracticalTimeSeriesAnalysis/BookRepo] 主要内容可见作者的SciPy 2019的讲座,看视频比较省事 [https://www.youtube.com/watch?v=v5ijNXvlC5A] (含slides与代码) 主要介绍了time series处理的各类方法 - 传统统计方法: ARIMA - State Model: HMM ...

用户评价

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作为介绍不错,但看完了还是"不会"使用时间序列; 感觉缺乏一些足够深入的例子

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作为介绍不错,但看完了还是"不会"使用时间序列; 感觉缺乏一些足够深入的例子

评分

作为介绍不错,但看完了还是"不会"使用时间序列; 感觉缺乏一些足够深入的例子

评分

作为介绍不错,但看完了还是"不会"使用时间序列; 感觉缺乏一些足够深入的例子

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突出了时间序列预测的机器学习方法。工具上混用了R和python,其中用到的R工具太老了

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