Explainable Failure Predictions with RNN Classifiers based on Time Series Data
This addresses the need for explainable failure predictions in storage systems, offering an incremental improvement by adding explainability to existing RNN methods.
The paper tackles the problem of predicting and explaining failures in storage environments using time series data, achieving comparable accuracy to traditional RNN classifiers within a 3-day prediction window while providing explanations through key anomalous events.
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.