Description
Time series data provides helpful insights about trends, seasonality and variance in applications varying from finance to the environment to personal sensors. But what do you do when you have hundreds or thousands of time series?
Enter time series clustering. In this talk, Susan Devitt, Senior Data Scientist, will introduce two algorithms for time series clustering - Shape Based Distance and Dynamical Time Warp - implemented in the python package dtwclust. Taking a real world example using daily financial transaction data, she will show you when to consider clustering and how it can be used for anomaly detection.