Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams

2026-01-08

arXiv.org

AI Summary

This work proposes TimeCast, a novel framework for real-time time-to-failure prediction in multi-sensor data streams characterized by dynamically evolving patterns. TimeCast addresses this challenge by online identification of dynamic evolution phases within the data stream and constructing dedicated predictive models for each phase, thereby adaptively capturing time-varying dependencies among sensors. The framework innovatively integrates dynamic phase detection with phase-aware modeling, leveraging a linearly scalable online learning algorithm to achieve high prediction accuracy while substantially reducing computational overhead. Experimental evaluations on real-world datasets demonstrate that TimeCast significantly outperforms existing methods in terms of prediction accuracy for event occurrence time, capability in identifying dynamic changes, and computational efficiency.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• time-to-event prediction
• multi-sensor data streams
• dynamic patterns
• machine failure prediction
• real-time forecasting

Innovation

• dynamic time-to-event prediction
• multi-sensor data streams
• adaptive modeling
• online learning
• scalable streaming algorithm