  Hybrid methods have been shown to outperform pure statistical and pure deep
learning methods at both forecasting tasks, and at quantifying the uncertainty
associated with those forecasts (prediction intervals). One example is
Multivariate Exponential Smoothing Long Short-Term Memory (MES-LSTM), a hybrid
between a multivariate statistical forecasting model and a Recurrent Neural
Network variant, Long Short-Term Memory. It has also been shown that a model
that ($i$) produces accurate forecasts and ($ii$) is able to quantify the
associated predictive uncertainty satisfactorily, can be successfully adapted
to a model suitable for anomaly detection tasks. With the increasing ubiquity
of multivariate data and new application domains, there have been numerous
anomaly detection methods proposed in recent years. The proposed methods have
largely focused on deep learning techniques, which are prone to suffer from
challenges such as ($i$) 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, and ($iv$) understanding of the
root causes of anomaly occurrences inhibited by the predominantly black-box
nature of deep learning methods. In this article, an extension of MES-LSTM is
presented, an interpretable anomaly detection model that overcomes these
challenges. With a focus on renewable energy generation as an application
domain, the proposed approach is benchmarked against the state-of-the-art. The
findings are that MES-LSTM anomaly detector is at least competitive to the
benchmarks at anomaly detection tasks, and less prone to learning from spurious
effects than the benchmarks, thus making it more reliable at root cause
discovery and explanation.
