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Time Series Indexing

You're reading from   Time Series Indexing Implement iSAX in Python to index time series with confidence

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781838821951
Length 248 pages
Edition 1st Edition
Languages
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Author (1):
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Mihalis Tsoukalos Mihalis Tsoukalos
Author Profile Icon Mihalis Tsoukalos
Mihalis Tsoukalos
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Table of Contents (11) Chapters Close

Preface 1. Chapter 1: An Introduction to Time Series and the Required Python Knowledge 2. Chapter 2: Implementing SAX FREE CHAPTER 3. Chapter 3: iSAX – The Required Theory 4. Chapter 4: iSAX – The Implementation 5. Chapter 5: Joining and Comparing iSAX Indexes 6. Chapter 6: Visualizing iSAX Indexes 7. Chapter 7: Using iSAX to Approximate MPdist 8. Chapter 8: Conclusions and Next Steps 9. Index 10. Other Books You May Enjoy

Storing an iSAX index in JSON format

For the visualizations of this chapter, we are going to use the low-level D3.js JavaScript library.

Is D3.js the only way to create visualizations?

The powerful D3.js JavaScript library is not a panacea and therefore, it is not the only way to create visualizations. There exist many Python packages that are good at plotting data, as well as programming languages such as R or Julia. However, JavaScript can be used for presenting your plots in a web page environment, which is not usually the case with the other options.

For the JavaScript D3.js code to work, we need to represent an iSAX index in JSON format so that it can be understood by the JavaScript code – we mainly need to represent the structure and the connections between iSAX nodes in a way that can be understood by a computer and a programming language. Therefore, the first step we should take is to convert an iSAX index representation with its structures from Python code...

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