What this book covers
Chapter 1, An Introduction to Time Series and the Required Python Knowledge, is all about the fundamentals that you need to know to follow this book, including the importance of time series and how to set up a proper Python environment to run the code of the book and experiment with time series.
Chapter 2, Implementing SAX, explains SAX and the SAX representation and presents Python code for computing the SAX representation of a time series or a subsequence. It also presents Python scripts that calculate statistical quantities that can give a higher overview of a time series and plot histograms of your time series data.
Chapter 3, iSAX – The Required Theory, presents the theory behind the construction and the use of the iSAX index and shows how to manually construct a small iSAX index step by step using lots of visualizations.
Chapter 4, iSAX - The Implementation, is about developing a Python package for creating iSAX indexes that fit in memory and presents Python scripts that put that Python package into action.
Chapter 5, Joining and Comparing iSAX Indexes, shows how to use iSAX indexes created by the isax package and how to join and compare them. At the end of the chapter, the subject of testing Python code is discussed. Last, we show how to write some simple tests for the isax package.
Chapter 6, Visualizing iSAX Indexes, is all about visualizing iSAX indexes using various types of visualizations using the JavaScript programming language and the JSON format.
Chapter 7, Using iSAX to Approximate MPdist, is about using iSAX indexes to approximately compute the Matrix Profile vectors and the MPdist distance between two time series.
Chapter 8, Conclusions and Next Steps, gives you directions on what and where to look next if you are really into time series or databases by proposing classical books and research papers to study.
