# Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques

arXiv: https://arxiv.org/abs/2308.03171 · HTML: https://mlsanity.com/paper/2308.03171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art](https://mlsanity.com/paper/2004.00433/) (2020)
- [Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data](https://mlsanity.com/paper/2202.05538/) (2022)

## Built on by (most cited first, 2 of 2)

- [An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals](https://mlsanity.com/paper/2508.09162/) (2025)
- [A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30](https://mlsanity.com/paper/2607.14391/) (2026)

## Abstract (arXiv)

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.