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)

Summary

Although the main purpose of iSAX is to help us search for subsequences by indexing them, there are other ways to use an iSAX index.

In this chapter, we presented a way to approximately compute the Matrix Profile vectors and two ways to approximately compute the MPdist distance between two time series. All these techniques use iSAX indexes.

We presented two ways to approximately compute MPdist. Out of the two methods, the one that joins two iSAX indexes is much more efficient than the other – so the use of an iSAX index by itself does not guarantee efficiency; we have to use an iSAX index the right way to get better results.

There is a small chapter left to finish this book, which is about the next steps you can follow if you are really into time series and databases.