Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
arXiv:2602.15089v1 Announce Type: new 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
arXiv:2602.15089v1 Announce Type: new 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.
Executive Summary
This article proposes a hybrid approach for equipment anomaly prediction in predictive maintenance using a combination of time series embeddings and statistical features. The approach integrates 64-dimensional time series embeddings from Granite TinyTimeMixer with 28-dimensional statistical features based on domain knowledge and uses a LightGBM gradient boosting classifier. The study demonstrates the effectiveness of the system with precision of 91-95% and ROC-AUC of 0.995 for anomaly prediction at 30-day, 60-day, and 90-day horizons. The approach has significant implications for practical anomaly detection systems, enabling the realization of production-ready performance with a false positive rate of 1.1% or less and a detection rate of 88-94%. The study highlights the complementary strengths between deep learning's representation learning capabilities and statistical feature engineering.
Key Points
- ▸ The proposed hybrid approach combines time series embeddings and statistical features for equipment anomaly prediction.
- ▸ The study achieves high precision and ROC-AUC values for anomaly prediction at various horizons.
- ▸ The approach realizes production-ready performance with a low false positive rate and high detection rate.
Merits
Strength in Representation Learning
The combination of time series embeddings and statistical features leverages the strengths of deep learning's representation learning capabilities and domain knowledge-based feature engineering, resulting in accurate anomaly prediction.
Improved Performance
The study demonstrates significant improvement in precision and ROC-AUC values compared to pure deep learning approaches, highlighting the effectiveness of the hybrid approach.
Production-Ready Performance
The approach achieves production-ready performance with a low false positive rate and high detection rate, making it suitable for real-world applications.
Demerits
Limited Generalizability
The study focuses on a specific domain (HVAC equipment) and may not generalize to other types of equipment or domains.
Complexity of Hybrid Approach
The proposed hybrid approach requires expertise in both deep learning and statistical feature engineering, which may be a barrier to adoption.
Limited Comparison with Other Methods
The study does not provide a comprehensive comparison with other state-of-the-art methods, making it difficult to evaluate the approach's relative performance.
Expert Commentary
The study presents a significant contribution to the field of predictive maintenance, demonstrating the effectiveness of a hybrid approach that combines time series embeddings and statistical features for anomaly prediction. The results are impressive, with high precision and ROC-AUC values for anomaly prediction at various horizons. However, the study's limitations, such as limited generalizability and complexity of the hybrid approach, need to be addressed in future research. The implications of the study are far-reaching, with potential applications in various domains where anomaly prediction is critical.
Recommendations
- ✓ Future studies should investigate the generalizability of the proposed approach to other domains and types of equipment.
- ✓ The development of more interpretable and transparent models is necessary to improve the trustworthiness of anomaly prediction models.
