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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 solve the following exercises in Python:

  • Divide by hand the y axis for the 16 = 24 cardinality. Did you divide it into 16 areas or 17 areas? How many breakpoints did you use?
  • Divide by hand the y axis for the 64 = 26 cardinality. Did you divide it into 64 areas?
  • Use the cardinality.py utility to plot the breakpoints of the 16 = 24 cardinality.
  • Use the cardinality.py utility to plot the breakpoints of the 128 = 2 7 cardinality.
  • Find the SAX representation of the {0, 2, -1, 2, 3, 4, -2, 4} subsequence using 4 segments and a cardinality of 4 (2 2). Do not forget to normalize it first.
  • Find the SAX representation of the {0, 2, -1, 2, 3, 4, -2, 4} subsequence using 2 segments and a cardinality of 2 (2 1). Do not forget to normalize it first.
  • Find the SAX representation of the {0, 2, -1, 2, 3, 1, -2, -4} subsequence using 4 segments and a cardinality of 2.
  • Given the {0, -1, 1.5, -1.5, 0, 1, 0} time series and a sliding window size of 4, find the SAX representation of all its subsequences using 2 segments and a cardinality of 2.
  • Create a synthetic time series and process it using using_tsfresh.py.
  • Create a synthetic time series with 1,000 elements and process it using histogram.py.
  • Create a synthetic time series with 5,000 elements and process it using histogram.py.
  • Create a synthetic time series with 10,000 elements and process it using counting.py.
  • Create a synthetic time series with 100 elements and process it using percentiles.py.
  • Create a synthetic dataset with 100 elements and examine it using counting.py.
  • Modify histogram.py to save its graphical output in a PNG file.
  • Plot a time series using histogram.py and then process it using using_tsfresh.py
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Time Series Indexing
Published in: Jun 2023
Publisher: Packt
ISBN-13: 9781838821951
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