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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

Checking the search speed of iSAX indexes

This section presents a utility that takes two time series, named TS1 and TS2, which ideally have similar lengths, creates two iSAX indexes, named D1 and D2, and performs the following searches:

  • Searches D1 for all the subsequences of TS2. In this case, we are not sure whether a subsequence from TS2 is in D1 or not. In most cases, we are not going to be able to find the subsequences of TS2 in TS1. This is the main reason that a join based on the SAX representations of the iSAX nodes might be more appropriate when looking for similarities between subsequences.
  • Searches D2 for all the subsequences of TS1. In this case, we are not sure whether a subsequence from TS1 is in D2 or not. As before, in most cases, we are not going to be able to find the subsequences of TS1 in TS2 and therefore, in the iSAX index created from TS2 (D2).
  • Searches D1 for all the subsequences of TS1, which means that all subsequences of TS1 are in D1. With...
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