Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction

• URL: http://arxiv.org/abs/2602.15089v1
• Date: Mon, 16 Feb 2026 15:00:15 GMT
• Title: Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
• Authors: Takato Yasuno,
• Abstract summary: This study proposes a hybrid approach that integrates 64-dimensional time series embeddings from Granite TinyTimeMixer with 28-dimensional statistical features.<n>In experiments using 64 equipment units and 51,564 samples, we achieved Precision of 91--95% and ROC-AUC of 0.995 for anomaly prediction at 30-day, 60-day, and 90-day horizons.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hybrid approach that integrates 64-dimensional time series embeddings from Granite TinyTimeMixer with 28-dimensional statistical features based on domain knowledge for HVAC equipment anomaly prediction tasks. Specifically, we combine time series embeddings extracted from a Granite TinyTimeMixer encoder fine-tuned with LoRA (Low-Rank Adaptation) and 28 types of statistical features including trend, volatility, and drawdown indicators, which are then learned using a LightGBM gradient boosting classifier. In experiments using 64 equipment units and 51,564 samples, we achieved Precision of 91--95\% and ROC-AUC of 0.995 for anomaly prediction at 30-day, 60-day, and 90-day horizons. Furthermore, we achieved production-ready performance with a false positive rate of 1.1\% or less and a detection rate of 88--94\%, demonstrating the effectiveness of the system for predictive maintenance applications. This work demonstrates that practical anomaly detection systems can be realized by leveraging the complementary strengths between deep learning's representation learning capabilities and statistical feature engineering.

Related papers

• Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines with Learned Aleatoric Uncertainty [0.0]
  This research introduces a novel uncertainty-aware deep learning framework that learns aleatoric uncertainty directly through probabilistic modeling.<n>Our framework achieves breakthrough critical zone performance (RUL = 30 cycles) with RMSE of 5.14, 6.89, 5.27, and 7.16.<n>The learned uncertainty provides well-calibrated 95 percent confidence intervals with coverage ranging from 93.5 percent to 95.2 percent, enabling risk-aware maintenance scheduling.
  arXiv  Detail & Related papers  (2025-11-24T13:53:31Z)
• Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting [40.9030781267984]
  We propose a novel test-time computing paradigm, namely learning with calibration, ST-TTC, for S-temporal forecasting.<n>We aim to capture periodic structural biases arising from non-stationarity during the testing phase and perform real-time bias correction on predictions to improve accuracy.
  arXiv  Detail & Related papers  (2025-05-31T16:48:27Z)
• Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study [2.251010251400407]
  This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition.<n>Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps as data vectors.<n>The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51%.
  arXiv  Detail & Related papers  (2025-03-07T17:53:29Z)
• Predictive Crash Analytics for Traffic Safety using Deep Learning [0.0]
  This research presents an innovative approach to traffic safety analysis through the integration of ensemble learning methods and multi-modal data fusion.<n>Our primary contribution lies in developing a hierarchical severity classification system that combines spatial-temporal crash patterns with environmental conditions.<n>We introduce a novel feature engineering technique that integrates crash location data with incident reports and weather conditions, achieving 92.4% accuracy in risk prediction and 89.7% precision in hotspot identification.
  arXiv  Detail & Related papers  (2025-02-09T05:00:46Z)
• Improved Anomaly Detection through Conditional Latent Space VAE Ensembles [49.1574468325115]
  Conditional Latent space Variational Autoencoder (CL-VAE) improved pre-processing for anomaly detection on data with known inlier classes and unknown outlier classes. Model shows increased accuracy in anomaly detection, achieving an AUC of 97.4% on the MNIST dataset. In addition, the CL-VAE shows increased benefits from ensembling, a more interpretable latent space, and an increased ability to learn patterns in complex data with limited model sizes.
  arXiv  Detail & Related papers  (2024-10-16T07:48:53Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection [44.21198064126152]
  We propose a novel anomaly detection framework named ImDiffusion. ImDiffusion combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:57:40Z)
• A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts [0.0]
  Post-processing typically takes the outputs of a Numerical Weather Prediction (NWP) model and applies linear statistical techniques. We show that we can achieve accuracy improvements of up to 12% (RMSE) in a field such as temperature at 850hPa for a 7 day forecast. We discuss the challenges of using standard metrics such as root mean squared error (RMSE) or anomaly correlation coefficient (ACC)
  arXiv  Detail & Related papers  (2023-03-28T20:48:01Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Understanding of the properties of neural network approaches for transient light curve approximations [37.91290708320157]
  This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients.
  arXiv  Detail & Related papers  (2022-09-15T18:00:08Z)
• Cloud Failure Prediction with Hierarchical Temporary Memory: An Empirical Assessment [64.73243241568555]
  Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex. This paper presents the first systematic study that assesses HTM in the context of failure prediction.
  arXiv  Detail & Related papers  (2021-10-06T07:09:45Z)
• Active machine learning for spatio-temporal predictions using feature embedding [0.537133760455631]
  Active learning could contribute to solving environmental problems through improved critical-temporal predictions. Here, we propose a novel batch AL method that fills this gap. We encode and cluster features of candidate data points, and query the best data based on the distance of embedded features to their cluster centers.
  arXiv  Detail & Related papers  (2020-12-08T12:55:29Z)

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.