Time series data is at the heart of many applications, from finance and system monitoring to weather forecasting and medical data analysis. "Time Series Indexing" offers a hands-on guide to implementing and leveraging the iSAX indexing technique in Python to efficiently manage, search, and analyze time series data. What this Book will help me do Gain the know-how to implement algorithms like SAX and iSAX with illustrative Python examples. Learn to construct robust time series indexes tailored to real-world data sets. Understand the theoretical underpinnings of time series processing and indexing techniques. Explore and employ visualization techniques to interpret time series structures and insights. Gain the skills to adapt iSAX methodologies to other programming environments and practices. Author(s) Mihalis Tsoukalos is an accomplished developer and author specializing in Python programming and data processing techniques. With years of experience translating complex academic research into practical applications, Mihalis excels at bridging the gap between theory and practice. His writing approach ensures readers grasp both the foundational principles and the hands-on methods needed to succeed. Who is it for? This book best suits researchers, analysts, and developers who work with time series data and seek to elevate their proficiency in indexing and managing such data. It is perfect for professionals with a foundational knowledge of Python and programming concepts. This material also supports learners eager to derive actionable insights from theory-heavy academic research.