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.
Table of Contents (11 chapters)

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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