# Automatic Anomaly Detection in the Cloud Via Statistical Learning

arXiv: https://arxiv.org/abs/1704.07706 · HTML: https://mlsanity.com/paper/1704.07706/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 25 of 25)

- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2307.00754/) (2023)
- [CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms](https://mlsanity.com/paper/2111.03753/) (2021)
- [Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain](https://mlsanity.com/paper/2310.05063/) (2023)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [Automatic Database Configuration Debugging using Retrieval-Augmented Language Models](https://mlsanity.com/paper/2412.07548/) (2024)
- [How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?](https://mlsanity.com/paper/2102.06560/) (2021)
- [SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series](https://mlsanity.com/paper/2104.09968/) (2021)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series](https://mlsanity.com/paper/2303.00409/) (2023)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition](https://mlsanity.com/paper/2109.08800/) (2021)
- [NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2101.02908/) (2021)
- [NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series](https://mlsanity.com/paper/2304.06168/) (2023)
- [Multimodal video analysis for crowd anomaly detection using open access tourism cameras](https://mlsanity.com/paper/2405.12708/) (2024)
- [Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2112.04704/) (2021)
- [Practical data monitoring in the internet-services domain](https://mlsanity.com/paper/2203.08067/) (2022)
- [Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems](https://mlsanity.com/paper/2207.03934/) (2022)
- [LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing](https://mlsanity.com/paper/2305.12266/) (2023)
- [k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection](https://mlsanity.com/paper/2311.08422/) (2023)
- [GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning](https://mlsanity.com/paper/2405.09561/) (2024)
- [Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domains](https://mlsanity.com/paper/2510.03486/) (2025)
- [StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R](https://mlsanity.com/paper/2510.04974/) (2025)

## Abstract (arXiv)

Performance and high availability have become increasingly important drivers, amongst other drivers, for user retention in the context of web services such as social networks, and web search. Exogenic and/or endogenic factors often give rise to anomalies, making it very challenging to maintain high availability, while also delivering high performance. Given that service-oriented architectures (SOA) typically have a large number of services, with each service having a large set of metrics, automatic detection of anomalies is non-trivial. Although there exists a large body of prior research in anomaly detection, existing techniques are not applicable in the context of social network data, owing to the inherent seasonal and trend components in the time series data. To this end, we developed two novel statistical techniques for automatically detecting anomalies in cloud infrastructure data. Specifically, the techniques employ statistical learning to detect anomalies in both application, and system metrics. Seasonal decomposition is employed to filter the trend and seasonal components of the time series, followed by the use of robust statistical metrics -- median and median absolute deviation (MAD) -- to accurately detect anomalies, even in the presence of seasonal spikes. We demonstrate the efficacy of the proposed techniques from three different perspectives, viz., capacity planning, user behavior, and supervised learning. In particular, we used production data for evaluation, and we report Precision, Recall, and F-measure in each case.