Practical Time Series Analysis : Prediction with Statistics and Machine Learning (True AZW3)

[LeeAndro]

English | 2019 | ISBN: 1492041653 | 466 pages | True AZW3 | 5.78 MB

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 series analysis with both statistical and machine learning techniques will increase.

Covering innovations in series data analysis and use cases from the real world, this practical guide will help you solve the most common data eeering and analysis challengesin series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to series in both R and Python that will have data scientists, software eeers, and researchers up and running quickly.

You'll get the guidance you need to confidently

- Find and wrangle series data
- Undertake exploratory series data analysis
- Store temporal data
- Simulate series data
- Generate and select features for a series
- Measure error
- Forecast and classify series with machine or deep learning
- Evaluate accuracy and performance



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