Practical Predictive Analytics on Time Series Data using SAX

Practical Predictive Analytics on Time Series Data using SAX: The potential to do machine learning on the data generated by connected sensors is a key factor that is driving the spread of the Internet of Things. Predictive analytics on time series data can be used to anticipate adverse events, enable early-warning systems, improve outcomes, reduce costs, and increase efficiency. In this talk you’ll learn how to use Symbolic Aggregate approXimation (SAX) to determine normal behavior, recognize behavior that is anomalous, quantify it, and classify it based on known patterns. In this presentation, Ray Richardson will walk you through the basics of SAX, and cover a predictive maintenance example in detail. One of the key advantages to using SAX is that it yields an explainable model. When the result of an analysis is designed to get someone to take an action, the importance of having an explainable model should not be underestimated. Key takeaways from this talk include: two methods to determine what normal is how to do time series classification how to predict time to failure open source SAX tools

View the slides for this session