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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

iSAX – The Required Theory

Now that we know all about SAX, including normalization and computing the SAX representation of a subsequence, it is time to learn the theory behind the iSAX index, which, at the time of writing, is considered one of the best time-series indexes. Improved versions of iSAX that make iSAX faster and more compact exist, but the core ideas remain the same.

As you might have guessed from its name, iSAX depends on SAX in some way. Put simply, the keys to every iSAX index are SAX representations. Therefore, searching in an iSAX index depends on SAX representations.

At this point, I believe it would be good to provide more information about iSAX to help you while reading this chapter. An iSAX index is a tree-like structure where the root, and only the root, can have multiple children, and all the children of the root are binary trees underneath. Additionally, to create an iSAX index, we need a time series and a threshold value, which is the maximum number...

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