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)

Understanding time series

A time series is a set of data. Keep in mind that a time series does not have to contain time or date data in it – time and date data usually come in the form of timestamps. So, a time series might contain timestamps, but usually, it does not. In fact, most of the time series in this book do not contain timestamps. In practice, what we really need is ordered data – this is what makes a bunch of values a time series.

Strictly speaking, a time series (T) of size n is an ordered list of data points: T = { t 0, t 1, t 2, t n1}. Data points can be timestamped and store a single value, a set of values, or a list of values. The index of a time series might begin with 1 instead of 0 – in this case, T = { t 1, t 2, t 3, t n}. What is truly important here is that the length of the time series is n in both cases. So, each element has an index value associated with it, which replaces...