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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

Joining and Comparing iSAX Indexes

In the previous chapter, we developed a Python package called isax that creates iSAX indexes for indexing the subsequences of a time series given a sliding window.

In this chapter, we are going to experiment with how the sliding window size affects the number of splits and the number of accesses to subsequences while creating an iSAX index.

Then, we are going to use the iSAX indexes created by the isax package and try to join and compare them. By comparing, we aim to understand the efficiency of an iSAX index, and by joining, we mean being able to find similar nodes in two iSAX indexes based on SAX representations.

The last part of this chapter is going to briefly discuss Python testing before developing simple tests for the isax package. Testing is a serious part of the development process and should not be overlooked. The time spent writing tests is time well spent!

In this chapter, we are going to cover the following main topics:

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