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[2602.15089] Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
Summary
This study presents a hybrid approach for equipment anomaly prediction by combining time series embeddings with statistical features, achieving high accuracy in predictive maintenance tasks.
Why It Matters
The integration of deep learning and statistical methods enhances the reliability of anomaly detection in real-world applications, particularly in predictive maintenance for HVAC systems. This research addresses the limitations of pure deep learning models, offering a practical solution with proven performance metrics.
Key Takeaways
- Hybrid model combines deep learning embeddings and statistical features for improved anomaly detection.
- Achieved high precision (91-95%) and ROC-AUC (0.995) in experiments.
- Demonstrates effectiveness for predictive maintenance in HVAC equipment.
- False positive rate maintained at 1.1% or less, ensuring reliability.
- Highlights the importance of integrating different methodologies for practical applications.
Computer Science > Machine Learning arXiv:2602.15089 (cs) [Submitted on 16 Feb 2026] Title:Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction Authors:Takato Yasuno View a PDF of the paper titled Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction, by Takato Yasuno View PDF HTML (experimental) 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...
