Practical Time Series Analysis: Prediction with Statistics and Machine Learning 1st Edition 29909

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

About the Author

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.

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, добре-rounded introduction to time series in both R Python and 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 machine with or deep learning
Evaluate accuracy and performance

About the Author

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 to load 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.