Occupancy detection

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:

• Aggregations over time, such as the average value of some column for the last 3 days.
• Seasonal effects, such as today is a Wednesday, so let's get the average value for the last four Wednesdays.
• Lag variables, such as get the value of some column from two hours ago.

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.

Data set

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:

• Date, year-month-day hour:minute:second
• Temperature, in Celsius
• Relative Humidity, %
• Light, in Lux
• CO2, in ppm
• Humidity Ratio, Derived quantity from temperature and relative humidity, in kgwater-vapor/kg-air
• Occupancy, 0 or 1, 0 for not occupied, 1 for occupied status

Result

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.