  It has been shown that deep learning models can under certain circumstances
outperform traditional statistical methods at forecasting. Furthermore, various
techniques have been developed for quantifying the forecast uncertainty
(prediction intervals). In this paper, we utilize prediction intervals
constructed with the aid of artificial neural networks to detect anomalies in
the multivariate setting. Challenges with existing deep learning-based anomaly
detection approaches include $(i)$ large sets of parameters that may be
computationally intensive to tune, $(ii)$ returning too many false positives
rendering the techniques impractical for use, $(iii)$ requiring labeled
datasets for training which are often not prevalent in real life. Our approach
overcomes these challenges. We benchmark our approach against the oft-preferred
well-established statistical models. We focus on three deep learning
architectures, namely, cascaded neural networks, reservoir computing and long
short-term memory recurrent neural networks. Our finding is deep learning
outperforms (or at the very least is competitive to) the latter.
