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)

Reading time series from disk

After storing a time series in a file, we need to write the necessary Python code to read it and put it in a Python variable of some type. This section will teach you exactly that. The read_ts.py script contains the following code:

#!/usr/bin/env python3
import pandas as pd
import numpy as np
import sys
def main():
        filename = sys.argv[1]
        ts1Temp = pd.read_csv(filename, header = None)
        # Convert to NParray
        ta = ts1Temp.to_numpy()
        ta = ta.reshape(len(ta))
        print("Length:", len(ta))
if __name__ == '__main__':
        main()

After reading the time series, read_ts.py prints the number of elements in the time series:

$ ./read_ts.py ts2
Length: 50

The pd.read_csv() function reads a plain text file that uses the CSV format – in our case, each value is on its own line, so there should be no issues with separating values that reside on the same line. The pd.read_csv() function is...