The significance of time series data analysis has grown exponentially, driven by the proliferation of such data through the internet of things, the digitization of healthcare, and the emergence of smart cities. With the increasing prevalence of continuous monitoring and data collection, the demand for proficient time series analysis incorporating both statistical and machine learning techniques is on the rise.
This practical guide addresses the evolving landscape of time series data analysis, presenting real-world use cases and innovative approaches. It equips you to tackle common challenges in data engineering and analysis for time series data, employing a combination of traditional statistical methods and contemporary machine learning techniques. Aileen Nielsen, the author, provides an accessible and comprehensive introduction to time series analysis in both R and Python, facilitating a quick start for data scientists, software engineers, and researchers.
Key features of the guide include:
- Locating and manipulating time series data
- Conducting exploratory analysis on time series data
- Managing temporal data
- Simulating time series data
- Creating and selecting features for time series
- Assessing error
- Utilizing machine or deep learning for time series forecasting and classification
- Assessing accuracy and performance
This resource empowers you to navigate the complexities of time series data analysis with confidence, offering practical insights and techniques for leveraging both statistical and machine learning methodologies.
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See also
- Software Exorcism: A Handbook for Debugging and Optimizing Legacy Code by Bill Blunden (2003)
- Hormones and the Endocrine System: Textbook of Endocrinology by Bernhard Kleine (2016)
- Genetics: A Conceptual Approach 6e by Benjamin A. Pierce (2017)
- A Functional Approach to Java by Ben Weidig (2022)
- Calculus of variations and optimal control by Amol Sasan (2005)