A Reinforcement Learning and Time Series Forest Based Model Selection for Unsupervised Anomaly Detection Techniques in Time Series Electricity Consumption

Abstract

Anomaly Detection (AD) in time series applications is vital given the wide usage of time series data in the real-world setting. Usually, real-world anomalies are found in a variety of forms making them more difficult to detect. Various anomaly detection models were considered in research, where each of these models tends to apply a specific assumption for detecting anomalies revealing more sensitivity towards a specific kind of anomalies. Hence, it is required to build a model selection for different AD models that is capable of choosing, at each time step, the suitable anomaly detection model. Such a model selection would be able to take advantage of several AD models in an attempt to maximize the accuracy of time series anomaly detection. Knowing that ground truth labels in real-world applications are expensive and rare to obtain in large quantities, it is important to design a model selection for AD that does not rely explicitly on ground truth labels. In this regard, a model selection for six unsupervised AD models is proposed based on Time-Series Forest and Reinforcement Learning to choose dynamically an AD technique without relying heavily on ground truth labels. Experiments on two synthetic and real time series datasets have been applied. Results on the real dataset reveal that the proposed model selection outperforms the entire AD models in terms of the F1score metric. In the synthetic dataset, the proposed model, recording an F1score of 0.989, outperforms all the AD models except for KNN. Different reward function scenarios, including adaptive rewards with multiple exploration rates, have been also considered. The proposed AD model selection approach with its original reward function yields the best overall scores. Each of the six AD models implemented on three additional synthetic datasets having global, local, and clustered anomalies respectively, show distinct performance depending on the type of anomalies. This proves the significance of the proposed AD model selection framework which maintains a high F1score on the three datasets.