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.

翻译：给定从机器获取的实时传感器数据流，我们如何持续预测机器故障何时发生？本研究旨在通过分析多传感器数据流，持续预测未来事件的发生时间。现实世界数据流的一个关键特征是其动态性，即底层模式会随时间演变。为此，我们提出了TimeCast——一个动态预测框架，旨在适应这些变化并提供对未来事件时间的准确实时预测。我们提出的方法具有以下特性：(a) 动态性：能够识别不同的时变模式（即阶段）并为每个阶段学习独立模型，从而基于模式转变实现自适应预测；(b) 实用性：能够发现捕捉多传感器间时变相互依赖关系的有意义阶段，并提升预测性能；(c) 可扩展性：算法复杂度与输入规模呈线性关系，支持在数据流上进行在线模型更新。在真实数据集上的大量实验表明，TimeCast相比现有最优方法具有更高的预测精度，同时能以大幅减少的计算时间发现数据流中的动态变化。