Book Image

Time Series Indexing

By : Mihalis Tsoukalos
Book Image

Time Series Indexing

By: Mihalis Tsoukalos

Overview of this book

Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX. The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript. By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data.

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Time Series Indexing
Mihalis Tsoukalos
Jun, 2023 | 248 pages
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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share your thoughts
Download a free PDF copy of this book
1
Chapter 1: An Introduction to Time Series and the Required Python Knowledge
Chapter 1: An Introduction to Time Series and the Required Python Knowledge
Technical requirements
Understanding time series
What is an index and why do we need indexing?
The Python knowledge that we are going to need
Reading time series from disk
Visualizing time series
Working with the Matrix Profile
Exploring the MPdist distance
Summary
Resources and useful links
Exercises
Free Chapter
2
Chapter 2: Implementing SAX
Chapter 2: Implementing SAX
Technical requirements
The required theory
An introduction to SAX
Developing a Python package
Working with the SAX package
Counting the SAX representations of a time series
The tsfresh Python package
Creating a histogram of a time series
Calculating the percentiles of a time series
Summary
Useful links
Exercises
3
Chapter 3: iSAX – The Required Theory
Chapter 3: iSAX – The Required Theory
Technical requirements
Background information
Understanding how iSAX works
How iSAX is constructed
Manually constructing an iSAX index
Updating the counting.py utility
Summary
Useful links
Exercises
4
Chapter 4: iSAX – The Implementation
Chapter 4: iSAX – The Implementation
Technical requirements
A quick look at the iSAX Python package
Explaining the missing parts
Exploring the remaining files
Using the iSAX Python package
Summary
Useful links
Exercises
5
Chapter 5: Joining and Comparing iSAX Indexes
Chapter 5: Joining and Comparing iSAX Indexes
Technical requirements
How the sliding window size affects the iSAX construction speed
Checking the search speed of iSAX indexes
Joining iSAX indexes
Implementing the joining of iSAX indexes
Explaining the Python code
Using the Python code
Writing Python tests
Summary
Useful links
Exercises
6
Chapter 6: Visualizing iSAX Indexes
Chapter 6: Visualizing iSAX Indexes
Technical requirements
Storing an iSAX index in JSON format
Visualizing an iSAX index
Trying something radical
More iSAX index visualizations
Using icicle plots
Visualizing iSAX as a Collapsible Tree
Summary
Useful links
Exercises
7
Chapter 7: Using iSAX to Approximate MPdist
Chapter 7: Using iSAX to Approximate MPdist
Technical requirements
Understanding the Matrix Profile
Computing the Matrix Profile using iSAX
Understanding MPdist
Calculating MPdist using iSAX
Implementing the MPdist calculation in Python
Using the Python code
Summary
Useful links
Exercises
8
Chapter 8: Conclusions and Next Steps
Chapter 8: Conclusions and Next Steps
Concluding all that we have learned so far
Other variations of iSAX
Interesting research papers on time series
Interesting research papers on databases
Useful books
Summary
Useful links
Exercises
9
Index
Index
Why subscribe?
10
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Using the Python code

In this section, we are going to use the Python scripts that we have created.

Running apprMPdist.py using the two time series with 10,000 elements each that we created earlier in this chapter generates the following kind of output:

$ ./apprMPdist.py 10k1.gz 10k2.gz -s 3 -c 64 -t 500 -w 120
Max Cardinality: 64 Segments: 3 Sliding Window: 120 Threshold: 500 Default Promotion: False
MPdist: 351.27 seconds
Approximate MPdist: 12.603

Using a bigger sliding window size generates the following output:

$ ./apprMPdist.py 10k1.gz 10k2.gz -s 3 -c 64 -t 500 -w 300
Max Cardinality: 64 Segments: 3 Sliding Window: 300 Threshold: 500 Default Promotion: False
MPdist: 384.74 seconds
Approximate MPdist: 21.757

So, bigger sliding window sizes require more time. As before, this is because calculating Euclidean distances for bigger sliding window sizes is slower.

Executing joinMPdist.py produces the following output:

$ ./joinMPdist.py 10k1.gz 10k2.gz -s 3 -c 64...
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