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附件包含英文版和中文版两份PDF文档，以及Python代码。
Practical Time Series Analysis Prediction with Statistics and Machine Learning
by A. Nielsen
October 2019
ISBN: 9781492041641
O'Reilly Media, Inc.
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.
You’ll get the guidance you need to confidently:
Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data
Simulate time series data
Generate and select features for a time series
Measure error
Forecast and classify time series with machine or deep learning
Evaluate accuracy and performance
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