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Practical Time Series Analysis: Prediction with Statistics and Machine Learning

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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. Coverin 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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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. Coverin 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

57 review for Practical Time Series Analysis: Prediction with Statistics and Machine Learning

  1. 5 out of 5

    Bojan Tunguz

    This is a very "big picture" book on modern time series analysis as it is practiced in Data Science and related domains. It gives a general overview of the main techniques, problems, and issues that arise in this field. However, from my standpoint, it is as far removed from the "practical" introduction as a book of this kind can be. There are very few worked out examples, and most of the techniques in the book are not as up-to-date as one would have liked. This is unfortunate, as the time-series This is a very "big picture" book on modern time series analysis as it is practiced in Data Science and related domains. It gives a general overview of the main techniques, problems, and issues that arise in this field. However, from my standpoint, it is as far removed from the "practical" introduction as a book of this kind can be. There are very few worked out examples, and most of the techniques in the book are not as up-to-date as one would have liked. This is unfortunate, as the time-series analysis and predictive modeling are very hot topics, and there are innumerable practical applications in almost any area of modern data science. The book provides a decent general overviews, but in order to learn anything truly applied and practical, I would recommend that one looks at many good online tutorials. In particular, I'd strongly recommend taking a look at the time-series Kaggle competitions.

  2. 4 out of 5

    Matt Aadland

    I think this is a good introductory book to learn the basics of time series analysis and it would be a good companion to "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos If you were to only read one book, I'd pick Forecasting: Principles and Practice as it goes into much more foundational detail. I think this is a good introductory book to learn the basics of time series analysis and it would be a good companion to "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos If you were to only read one book, I'd pick Forecasting: Principles and Practice as it goes into much more foundational detail.

  3. 4 out of 5

    Lara Thompson

    There's a lot of undigested material in this book; many worked through examples with poor modelling results. There are multiple errors. There are many things covered however; many data sources suggested; many references given. I think a better editor and much more time to whittle this book into one with insight and not just so much blah blah that's all over the internet already. There's a lot of undigested material in this book; many worked through examples with poor modelling results. There are multiple errors. There are many things covered however; many data sources suggested; many references given. I think a better editor and much more time to whittle this book into one with insight and not just so much blah blah that's all over the internet already.

  4. 5 out of 5

    Xingda Wang

    Read until page 190, I find it somehow not clear especially on how model is setup and criterion analysis.

  5. 4 out of 5

    Chris King

    Read this if you are looking to learn about time series models.

  6. 5 out of 5

    William Hau

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    Alexey Olkov

  8. 5 out of 5

    Mumble

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    Piotr

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    Maria-Anna

  11. 4 out of 5

    Diana Gornea

  12. 4 out of 5

    Dan

  13. 5 out of 5

    Joshua D.

  14. 5 out of 5

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  15. 4 out of 5

    Kevin

  16. 4 out of 5

    J Sha

  17. 5 out of 5

    Hasan Basri AKIRMAK

  18. 5 out of 5

    Lordq

  19. 5 out of 5

    Amalia

  20. 4 out of 5

    David De Weerdt

  21. 5 out of 5

    Desi Todorova

  22. 5 out of 5

    Amalfi Darusman

  23. 4 out of 5

    michael o. duffy

  24. 5 out of 5

    Divya Jennifer

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    Szymon

  26. 4 out of 5

    Tom R.

  27. 4 out of 5

    Amanda

  28. 4 out of 5

    Krisztian Pillis

  29. 5 out of 5

    Nitin Agarwal

  30. 4 out of 5

    Bill Goldsworthy

  31. 4 out of 5

    Alex

  32. 4 out of 5

    Daniel Cunningham

  33. 5 out of 5

    Akbar

  34. 4 out of 5

    Lobster41

  35. 5 out of 5

    Sam Bacon

  36. 5 out of 5

    Sammy Yu

  37. 4 out of 5

    Pranav Shil

  38. 4 out of 5

    Joe Kostas

  39. 5 out of 5

    Soheyla Mirshahi

  40. 5 out of 5

    Elvis Asihene

  41. 4 out of 5

    Igor Vieira

  42. 4 out of 5

    Fernando S.

  43. 4 out of 5

    Sidy Danioko

  44. 4 out of 5

    Александр Коваленко

  45. 5 out of 5

    Lalo Steinmann

  46. 4 out of 5

    Hermann Rösch

  47. 4 out of 5

    Arash Ashrafzadeh

  48. 5 out of 5

    Damian Parmuchi

  49. 4 out of 5

    Jeremy Banta

  50. 4 out of 5

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  51. 4 out of 5

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  53. 5 out of 5

    Jawad

  54. 4 out of 5

    Martynas Puronas

  55. 4 out of 5

    Mal

  56. 5 out of 5

    Christian

  57. 4 out of 5

    Yang Yu

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