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)

Working with the Matrix Profile

In this section, as well as the next one, we will work with the stumpy Python package. This package is not related to iSAX but offers lots of advanced functionality related to time series. With the help of stumpy, we can calculate the Matrix Profile.

The Matrix Profile is two things:

  • A vector of distances that shows the distance of each subsequence in a time series to its nearest neighbor
  • A vector of indexes that shows the index of the nearest neighbor of each subsequence in a time series

The Matrix Profile can be used in many time series mining tasks. The main reason for presenting it is to understand that working with time series can be slow, so we need structures and techniques to improve the performance of time series-related tasks.

To get a better idea of the use of the Matrix Profile and the time it takes stumpy to calculate the Matrix Profile, here is the Python code of matrix_profile.py:

#!/usr/bin/env python3
import...