Code: USD75OFF
This is the code repository for Time Series Indexing, published by Packt.
Implement iSAX in Python to index time series with confidence
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.
This book covers the following exciting features:
- Find out how to develop your own Python packages and write simple Python tests
- Understand what a time series index is and why it is useful
- Gain a theoretical and practical understanding of operating and creating time series indexes
- Discover how to use SAX representation and the iSAX index
- Find out how to search and compare time series
- Utilize iSAX visualizations to aid in the interpretation of complex or large time series
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
def query(ISAX, q):
global totalQueries
totalQueries = totalQueries + 1
Accesses = 0
# Create TS Node
Following is what you need for this book: This book is for practitioners, university students working with time series, researchers, and anyone looking to learn more about time series. Basic knowledge of UNIX, Linux, and Python and an understanding of basic programming concepts are needed to grasp the topics in this book. This book will also be handy for people who want to learn how to read research papers, learn from them, and implement their algorithms.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
This book requires a UNIX machine with a relatively recent Python 3 installation and the ability to install Python packages locally. This includes any machine running recent versions of macOS and Linux. All the code has been tested on a Microsoft Windows machine. We propose that you use software for Python package, dependency, and environment management to have a stable Python 3 environment. We use Anaconda, but any similar tool is going to work fine. Last, if you really want to make the best use of the book, then you need to experiment as much as you can with the presented Python code, create your own iSAX indexes and visualizations, and maybe port the code into a different programming language
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Mihalis Tsoukalos holds a BSc in mathematics from the University of Patras and an MSc in IT from University College London, UK. His books Go Systems Programming and Mastering Go have become must-reads for Unix and Linux systems professionals. He enjoys writing technical articles and has written for Sys Admin, Mactech, C/C++ Users Journal, USENIX ;login:, Linux Journal, Linux User and Developer, Linux Format, and Linux Voice. His research interests include time series data mining, time series indexing, machine learning, and databases. Mihalis is also a photographer.