mlsanityData to 14 Oct 2026

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services

arXiv 2508.14503 ↗First posted 20 Aug 2025cs.LGcited by 6

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Abstract (arXiv)

This study proposes an anomaly detection method based on the Transformer architecture with integrated multiscale feature perception, aiming to address the limitations of temporal modeling and scale-aware feature representation in cloud service environments. The method first employs an improved Transformer module to perform temporal modeling on high-dimensional monitoring data, using a self-attention mechanism to capture long-range dependencies and contextual semantics. Then, a multiscale feature construction path is introduced to extract temporal features at different granularities through downsampling and parallel encoding. An attention-weighted fusion module is designed to dynamically adjust the contribution of each scale to the final decision, enhancing the model's robustness in anomaly pattern modeling. In the input modeling stage, standardized multidimensional time series are constructed, covering core signals such as CPU utilization, memory usage, and task scheduling states, while positional encoding is used to strengthen the model's temporal awareness. A systematic experimental setup is designed to evaluate performance, including comparative experiments and hyperparameter sensitivity analysis, focusing on the impact of optimizers, learning rates, anomaly ratios, and noise levels. Experimental results show that the proposed method outperforms mainstream baseline models in key metrics, including precision, recall, AUC, and F1-score, and maintains strong stability and detection performance under various perturbation conditions, demonstrating its superior capability in complex cloud environments.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 55 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

24 of 55 references.

References · 24

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  5. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
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  7. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  8. 2017Automatic Anomaly Detection in the Cloud Via Statistical Learningcited by 25
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  10. 2025Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimizationcited by 10
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  12. 2025Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaborationcited by 9
12 more
  1. 2025Context-Aware Rule Mining Using a Dynamic Transformer-Based Frameworkcited by 7
  2. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  3. 2025Clinical NLP with Attention-Based Deep Learning for Multi-Disease Predictioncited by 7
  4. 2025Autonomous Resource Management in Microservice Systems via Reinforcement Learningcited by 7
  5. 2025Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimationcited by 6
  6. 2025Decomposition-based multi-scale transformer framework for time series anomaly detectioncited by 5
  7. 2025State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordinationcited by 5
  8. 2025Collaborative Distillation Strategies for Parameter-Efficient Language Model Deploymentcited by 5
  9. 2025Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networkscited by 4
  10. 2025A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Datacited by 3
  11. 2025PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detectioncited by 2
  12. 2023EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Seriescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learningcited by 5
  2. 2025Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Datacited by 5
  3. 2025Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environmentscited by 4
  4. 2025Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Recordscited by 2
  5. 2025Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networkscited by 1
  6. 2026Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modelingcited by 0