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