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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

Computing the Matrix Profile using iSAX

First of all, let us make something clear: we are going to present an approximate method. If you want to calculate the exact Matrix Profile, then you should use an implementation that uses the original algorithm.

The idea behind the used technique is the following: it is more likely that the nearest neighbor of a subsequence is going to be found in the subsequences stored in the same terminal node as the subsequence under examination. Therefore, we do not need to check all the subsequences of the time series, just a small subset of them.

The next subsection discusses and resolves an issue that might come up in our calculations, which is what are we going to do if we cannot find a proper match for a subsequence in a terminal node.

What happens if there is not a valid match?

In this subsection, we are going to clarify the problematic cases of the process. There exist two conditions that might end up in an undesired situation:

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