Time Series Indexing: Implement iSAX in Python to index time series with confidence

Build and use the most popular time series index available today with Python to search and join time series at the subsequence level

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Learn how to implement algorithms and techniques from research papers
• Get to grips with building time series indexes using iSAX
• Leverage iSAX to solve real-world time series problems

Book Description

Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX.

The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript.

By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data.

What you will learn

• Find out how to develop your own Python packages and write simple Python tests
• Understand what a time series index is and why it is useful
• Gain a theoretical and practical understanding of operating and creating time series indexes
• Discover how to use SAX representation and the iSAX index
• Find out how to search and compare time series
• Utilize iSAX visualizations to aid in the interpretation of complex or large time series

Who this book is for

This book is for practitioners, university students working with time series, researchers, and anyone looking to learn more about time series. Basic knowledge of UNIX, Linux, and Python and an understanding of basic programming concepts are needed to grasp the topics in this book. This book will also be handy for people who want to learn how to read research papers, learn from them, and implement their algorithms.

Table of Contents

1. An Introduction to Time Series and the Required Python Knowledge
2. Implementing SAX
3. iSAX – The Required Theory
4. iSAX - The implementation
5. Joining and Comparing iSAX Indexes
6. Visualizing iSAX Indexes
7. Using iSAX to Approximate MPdist
8. Conclusions and Next Steps

Mihalis Tsoukalos holds a BSc in mathematics from the University of Patras and an MSc in IT from University College London, UK. His books Go Systems Programming and Mastering Go have become must-reads for Unix and Linux systems professionals. He enjoys writing technical articles and has written for Sys Admin, Mactech, C/C++ Users Journal, USENIX ;login:, Linux Journal, Linux User and Developer, Linux Format, and Linux Voice. His research interests include time series data mining, time series indexing, machine learning, and databases.

Mihalis is also a photographer