Clustering time-series. An overview about different application contexts of time-series clustering
Jun 1, 2015·
·
0 min read
Dr. Georg Heiler
R ecp packageAbstract
Time-series are becoming more and more important in the digitized industry 4.0. from forecasting of sales to increase the profit in retail industry, to real time streamed analysis for fraud detection, intrusion-detection, to medical applications e.g. combination of different time series (ECG, Blood, …) for improvement of diagnoses, to applications in stock market. This work presents an overview of different application contexts of time-series clustering as a very hands-on, tutorial-like approach. Clustering time-series is often used to gain insight into the generating mechanism of the data in order to predict future values.
Publication
Clustering time-series, BSc Thesis

Authors
senior data expert
Georg is a Senior data expert at Magenta and a ML-ops engineer at ASCII.
He is solving challenges with data. His interests include geospatial graphs
and time series. Georg transitions the data platform of Magenta to the cloud
and is handling large scale multi-modal ML-ops challenges at ASCII.