Hi, I have a use case where I like to analyze windows of sensordata. Currently I have a working case where I use Structured Streaming to process real-time streams of sensordata. Now I like to analyse windows of sensordata and use classification to predict the class of a whole window. For instance, the application receives batches of sensordata (where each record holds: timestamp, value, key). With the use of Machine learning I like to analyse windows of these streams and classify the window as ‘warm’ or ‘cold’. A single record is not sufficient for classification, a window of records shapes a pattern to be used for classification.
But how should you define features for a window of sensordata? Each value (sensor) as a separate feature in the vector (for a window of x seconds, the vector contains x sensor values)? Or is there a way a feature can hold multiple values (like an array)? Or use some kind of encoding to fit x sensor values as a single feature? Regards, Chris
