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

• URL: http://arxiv.org/abs/2601.04741v2
• Date: Thu, 15 Jan 2026 10:15:50 GMT
• Title: Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
• Authors: Kota Nakamura, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai,
• Abstract summary: This work aims to continuously forecast the timing of future events by analyzing multi-sensor data streams.<n>A key characteristic of real-world data streams is their dynamic nature, where the underlying patterns evolve over time.<n>We present TimeCast, a dynamic prediction framework designed to adapt to these changes and provide accurate, real-time predictions of future event time.
• Score: 15.63942084384363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Online Kernel Dynamic Mode Decomposition for Streaming Time Series Forecasting with Adaptive Windowing [3.6194446038160315]
  We introduce WORK-DMD (Windowed Online Random Kernel Dynamic Mode Decomposition), a method that combines Random Fourier Features with online Dynamic Mode Decomposition to capture nonlinear dynamics.<n> WORK-DMD employs Sherman-Morrison updates within rolling windows, enabling continuous adaptation to evolving dynamics from only current data.<n> Experiments on benchmark datasets across several domains show that WORK-DMD achieves higher accuracy than several state-of-the-art online forecasting methods.
  arXiv  Detail & Related papers  (2025-10-17T07:57:37Z)
• UNet with Axial Transformer : A Neural Weather Model for Precipitation Nowcasting [0.06906005491572399]
  We develop a novel method that employs Transformer-based machine learning models to forecast precipitation.<n>This paper represents an initial research on the dataset used in the domain of next frame prediciton.
  arXiv  Detail & Related papers  (2025-04-28T01:20:30Z)
• Modeling Time-evolving Causality over Data Streams [6.897244582507126]
  We present a novel streaming method, ModePlait, which is designed for modeling time-evolving causality in co-evolving data streams.<n>Our proposed model outperforms state-of-the-art methods in terms of discovering the time-evolving causality as well as forecasting.
  arXiv  Detail & Related papers  (2025-02-13T04:59:01Z)
• Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization [74.3339999119713]
  We develop a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies.<n>Our method first scales and decomposes the input time series, then thresholds and quantizes the wavelet coefficients, and finally pre-trains an autoregressive model to forecast coefficients for the forecast horizon.
  arXiv  Detail & Related papers  (2024-12-06T18:22:59Z)
• A Predictive Approach To Enhance Time-Series Forecasting [6.377828331013327]
  We introduce Future-Guided Learning, an approach that enhances time-series event forecasting.<n>Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data.<n>We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 23.4% reduction in MSE for forecasting in nonlinear dynamical systems.
  arXiv  Detail & Related papers  (2024-10-19T21:22:55Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks [31.10683149519954]
  We propose an innovative time series forecasting model TimeSieve. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T15:58:12Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset.<n>Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale.<n>We also introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Improved Predictive Deep Temporal Neural Networks with Trend Filtering [22.352437268596674]
  We propose a new prediction framework based on deep neural networks and a trend filtering. We reveal that the predictive performance of deep temporal neural networks improves when the training data is temporally processed by a trend filtering.
  arXiv  Detail & Related papers  (2020-10-16T08:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.