  Given key performance indicators collected with fine granularity as time
series, our aim is to predict and explain failures in storage environments.
Although explainable predictive modeling based on spiky telemetry data is key
in many domains, current approaches cannot tackle this problem. Deep learning
methods suitable for sequence modeling and learning temporal dependencies, such
as RNNs, are effective, but opaque from an explainability perspective. Our
approach first extracts the anomalous spikes from time series as events and
then builds an RNN classifier with attention mechanisms to embed the
irregularity and frequency of these events. A preliminary evaluation on real
world storage environments shows that our approach can predict failures within
a 3-day prediction window with comparable accuracy as traditional RNN-based
classifiers. At the same time it can explain the predictions by returning the
key anomalous events which led to those failure predictions.
