Practical Time Series Analysis: Prediction with Statistics and Machine Learning - Paperback

Master Time Series Data Analysis with Statistical and Machine Learning Techniques

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 challenges in 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.

What You'll Learn

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

Dual Programming Language Coverage

This book provides practical examples in both R and Python, allowing you to work with the tools most relevant to your workflow. Whether you're a data scientist, software engineer, or researcher, you'll find code examples and techniques applicable to your projects in IoT, healthcare analytics, smart cities, and other domains requiring temporal data analysis.