Practical Time Series Analysis: Prediction with Statistics and Machine Learning

Time series data analysis is becoming increasingly important due to the internet of things, digitalization of healthcare, and rise of smart cities. This practical guide covers innovations in time series data analysis and use cases from the real world, offering an accessible introduction to time series in R and Python for data scientists, software engineers, and researchers.

Format: Paperback / softback
Length: 400 pages
Publication date: 01 November 2019
Publisher: O'Reilly Media, Inc, USA

Due to the exponential growth in the generation of time series data driven by the Internet of Things, healthcare digitalization, and the emergence of smart cities, time series data analysis has become increasingly crucial. The demand for skilled time series analysis techniques that combine statistical and machine learning approaches will rise as continuous monitoring and data collection become more prevalent. This practical guide aims to address the most common challenges in time series data engineering and analysis by covering cutting-edge innovations and real-world use cases. Author Aileen Nielsen provides a comprehensive and accessible introduction to time series in both R and Python, enabling data scientists, software engineers, and researchers to quickly get up to speed. The guide covers essential topics such as locating, wrangling, exploratory analysis, storing temporal data, simulating time series, generating and selecting features for time series, measuring error, forecasting, and classifying time series using machine or deep learning. By following the guidance provided in this book, readers will gain confidence in finding, analyzing, and forecasting time series data, ultimately improving their ability to make informed decisions based on this valuable source of information.

Weight: 796g
Dimension: 233 x 180 x 26 (mm)
ISBN-13: 9781492041658