Detection of Anomalies in Multivariate Time Series Using Ensemble
  Techniques
Anastasios Iliopoulos John Violos Christos Diou Iraklis Varlamis
Anomaly Detection in multivariate time series is a major problem in many
fields. Due to their nature, anomalies sparsely occur in real data, thus making
the task of anomaly detection a challenging problem for classification
algorithms to solve. Methods that are based on Deep Neural Networks such as
LSTM, Autoencoders, Convolutional Autoencoders etc., have shown positive
results in such imbalanced data. However, the major challenge that algorithms
face when applied to multivariate time series is that the anomaly can arise
from a small subset of the feature set. To boost the performance of these base
models, we propose a feature-bagging technique that considers only a subset of
features at a time, and we further apply a transformation that is based on
nested rotation computed from Principal Component Analysis (PCA) to improve the
effectiveness and generalization of the approach. To further enhance the
prediction performance, we propose an ensemble technique that combines multiple
base models toward the final decision. In addition, a semi-supervised approach
using a Logistic Regressor to combine the base models' outputs is proposed. The
proposed methodology is applied to the Skoltech Anomaly Benchmark (SKAB)
dataset, which contains time series data related to the flow of water in a
closed circuit, and the experimental results show that the proposed ensemble
technique outperforms the basic algorithms. More specifically, the performance
improvement in terms of anomaly detection accuracy reaches 2% for the
unsupervised and at least 10% for the semi-supervised models.