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Time Series Indexing

You're reading from   Time Series Indexing Implement iSAX in Python to index time series with confidence

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781838821951
Length 248 pages
Edition 1st Edition
Languages
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Author (1):
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Mihalis Tsoukalos Mihalis Tsoukalos
Author Profile Icon Mihalis Tsoukalos
Mihalis Tsoukalos
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Table of Contents (11) Chapters Close

Preface 1. Chapter 1: An Introduction to Time Series and the Required Python Knowledge 2. Chapter 2: Implementing SAX FREE CHAPTER 3. Chapter 3: iSAX – The Required Theory 4. Chapter 4: iSAX – The Implementation 5. Chapter 5: Joining and Comparing iSAX Indexes 6. Chapter 6: Visualizing iSAX Indexes 7. Chapter 7: Using iSAX to Approximate MPdist 8. Chapter 8: Conclusions and Next Steps 9. Index 10. Other Books You May Enjoy

Visualizing time series

Most of the time, having a high-level overview of your data is an excellent way to get to know your data. The best way to get an overview of a time series is by visualizing it.

There are multiple ways to visualize a time series, including tools such as R or Matlab, or using a large amount of existing JavaScript packages. In this section, we are going to use a Python package called Matplotlib for visualizing the data. Additionally, we will save the output to a PNG file. A viable alternative to this is to use a Jupyter notebook – Jupyter comes with Anaconda – and display the graphical output on your favorite web browser.

The visualize.py script reads a plain text file with values – a time series – and creates a plot. The Python code of visualize.py is as follows:

#!/usr/bin/env python3
import sys
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
def main():
    if len(sys...
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