Concluding all that we have learned so far
Time series are everywhere! But time series tend to get larger and larger as we collect more data, more frequently. Therefore, we need ways to process and search large time series faster and faster in order to make useful deductions from the data.
The iSAX index is here to help you search your time series data fast. I hope that this book has given you the necessary tools and knowledge to begin working with time series and subsequences, as well as the iSAX index in Python. However, the knowledge and the presented techniques are easily transferable to other programming languages, including but not limited to Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript.
We believe that we have provided the right amount of knowledge about time series indexing using the right amount of theory and practice so you can successfully work with time series and develop iSAX indexes.
The next section presents improved versions of iSAX.
