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

- Preface
- 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
- Index