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• W2914274258 abstract "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." @default.
• W2914274258 created "2019-02-21" @default.
• W2914274258 creator A5014197053 @default.
• W2914274258 creator A5069005357 @default.
• W2914274258 date "2019-01-20" @default.
• W2914274258 modified "2023-09-25" @default.
• W2914274258 title "Explainable Failure Predictions with RNN Classifiers based on Time Series Data." @default.
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