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.

本研究采用深度学习方法，提取异常事件并构建带有注意力机制的 RNN 分类器，以预测并解释存储环境中的故障。结果表明，该方法具有与传统 RNN 基本分类器相当的准确性，并可通过返回导致故障预测的关键异常事件来解释预测结果。

基于时间序列数据的RNN分类器的可解释性故障预测