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 iSAX indexes

At this point, we have iSAX indexes that we want to use to perform basic time series data mining tasks. One of them is finding similar subsequences between two or more time series. In our case, we are working with two time series, but the method can be extended to more time series with small changes.

How to join iSAX indexes

Given two or more iSAX indexes, it is up to us to decide how and why we are going to join them. We can even join them using SAX representations with a cardinality value of 2. However, using the SAX representations of the nodes as our keys for the join is the most logical choice.In our case, we are going to use the iSAX indexes and the SAX representations of the nodes to look for similar subsequences. This is because we have the intuition that subsequences in nodes with the same SAX representation are close to each other. The term close is defined relative to a distance metric. For the purposes of this chapter, we are going to use the...