title: K-Means on time series data with Apache Spark
body: <p>I have a data pipeline system where all events are stored in Apache Kafka. There is an event processing layer, which consumes and transforms that data (time series) and then stores the resulting data set into Apache Cassandra.</p>
  
  <p>Now I want to use Apache Spark in order train some machine learning models for anomaly detection. The idea is to run the k-means algorithm on the past data for example for every single hour in a day. </p>
  
  <p>For example, I can select all events from 4pm-5pm and build a model for that interval. If I apply this approach, I will get exactly 24 models (centroids for every single hour). </p>
  
  <p>If the algorithm performs well, I can reduce the size of my interval to be for example 5 minutes.</p>
  
  <p>Is it a good approach to do anomaly detection on time series data?</p>