Practical Time Series Analysis

Practical Time Series Analysis
PDF, ePUB
  • eBook:
    Practical Time Series Analysis: Prediction with Statistics and Machine Learning
  • Author:
    Aileen Nielsen
  • Edition:
    1 edition
  • Categories:
  • Data:
    October 29, 2019
  • ISBN:
    1492041653
  • ISBN-13:
    9781492041658
  • Language:
    English
  • Pages:
    504 pages
  • Format:
    PDF, ePUB

Book Description
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

Content

1. Time Series: An Overview and a Quick History
2. Finding and Wrangling Time Series Data
3. Exploratory Data Analysis for Time Series
4. Simulating Time Series Data
5. Storing Temporal Data
6. Statistical Models for Time Series
7. State Space Models for Time Series
8. Generating and Selecting Features for a Time Series
9. Machine Learning for Time Series
10. Deep Learning for Time Series
11. Measuring Error
12. Performance Considerations in Fitting and Serving Time Series Models
13. Healthcare Applications
14. Financial Applications
15. Time Series for Government
16. Time Series Packages
17. Forecasts About Forecasting

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