The occupancy detection data set is a very simple multivariate time series. This is to demonstrate how relational learning can be successfully applied to time series.
The key is a self-join. Instead of creating features by merging and aggregating peripheral tables in a relational data model, we perform the same operations on the population table itself. This results in features like these:
Using getML's algorithms for relational learning, we can extract all of these features automatically. Having created a flat table of such features, we can then apply state-of-the-art machine learning algorithms, like xgboost.
This project is based on a public domain time series dataset. It is available in the UC Irvine Machine Learning Repository. The challenge is straightforward: We want to predict whether an office room is occupied at a given moment in time using sensor data. The data is measured about once a minute. The ground-truth occupancy was obtained from time-stamped pictures. The available columns are as follows:
This project demonstrates that relational learning is a powerful tool for time series. getML is able to outperform the benchmarks for a scientific paper on a simple public domain time series data set using relatively little effort.
FastProp is our unique take on propositionalization. In real-world benchmarks against popular propositionalization libraries, we find FastProp is between 34x to 179x faster than the current state of the art.
ZEISS is working on delivering highly reliable machines and support processes. GetML allows building new predictive maintenance applications in a fraction of the time.