Choose this use-case if you wish to develop a model that groups unlabelled timeseries data into clusters. Timeseries clustering may be useful in tracking patterns in stock price movement, energy consumption, or sensor data. Given a dataset indexed in time order, you can create a model designed specifically to locate and report inherent patterns and anomalies within temporal data.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Timeseries Clustering model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Timeseries Clustering model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.