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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

Exercises

Try to do the following exercises:

  • Create a new Anaconda environment.
  • List the installed packages of an Anaconda environment.
  • Delete an existing Anaconda environment.
  • Create a new synthetic dataset with 1,000 values from -10 to +10.
  • Create a new synthetic dataset with 100,000 values from 0 to +10.
  • Write a Python script that reads a plain text file line by line.
  • Write a Python script that reads a plain text file and prints it word by word. Why is this more difficult than printing a file line by line?
  • Write a Python script that reads the same plain text file multiple times, and time that operation. The number of times the file is read as well as the file path should be given as command-line arguments.
  • Modify synthetic_data.py to generate integer values instead of floating-point values.
  • Create a time series with 500,000 elements with synthetic_data.py, and execute matrix_profile.py on the generated time series. Do not forget to compress...
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