Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
This work addresses the challenge of real-time event prediction for industrial maintenance, though it appears incremental as it builds on existing methods with dynamic adaptations.
The paper tackles the problem of predicting machine failure times from real-time multi-sensor data streams by developing TimeCast, a dynamic framework that adapts to evolving patterns, resulting in higher prediction accuracy and reduced computational time compared to state-of-the-art methods.
Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of future events by analyzing multi-sensor data streams. A key characteristic of real-world data streams is their dynamic nature, where the underlying patterns evolve over time. To address this, we present TimeCast, a dynamic prediction framework designed to adapt to these changes and provide accurate, real-time predictions of future event time. Our proposed method has the following properties: (a) Dynamic: it identifies the distinct time-evolving patterns (i.e., stages) and learns individual models for each, enabling us to make adaptive predictions based on pattern shifts. (b) Practical: it finds meaningful stages that capture time-varying interdependencies between multiple sensors and improve prediction performance; (c) Scalable: our algorithm scales linearly with the input size and enables online model updates on data streams. Extensive experiments on real datasets demonstrate that TimeCast provides higher prediction accuracy than state-of-the-art methods while finding dynamic changes in data streams with a great reduction in computational time.