Temporal Pattern Discovery for Anomaly Detection in a Smart Home

The temporal nature of data collected in a smart environment provides us with a better understanding of patterns over time. Detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Temporal pattern discovery based on modified Allen’s temporal relations [5] has helped discover interesting patterns and relations on smart home datasets [10]. This paper describes a method of discovering temporal relations in smart home datasets and applying them to perform anomaly detection process on the frequently-occurring events. We also include experimental results, performed on real and synthetic datasets.