Advanced Time Series Anomaly Detector in Fabric
Anomaly Detector, one of Azure AI services, enables you to monitor and detect anomalies in your time series data. This service is being retired by October 2026, and as part of the migration process the anomaly detection algorithms were open sourced and published by a new Python package and we offer a time series anomaly detection workflow in Microsoft Fabric data platform.Integrating AI: Best Practices and Resources to Get Started
What are the problems you can solve with AI? How do you experiment and prototype? This article aims to help you decide if and how to integrate AI into your applications, get you started with Azure’s ready to use AI solutions, Cognitive Services and answer your most frequent questions when getting started.
Overview of SR-CNN algorithm in Azure Anomaly Detector
Author: Tony Xing (@XingGuodong), AI Platform, C + AI In the last blog “Introducing Azure Anomaly Detector API”, I didn't provide enough details on one of the algorithms. As the algorithm paper was in the publishing process. The paper was accepted by KDD 2019 for oral presentation later, and this blog serves as an overview of the SR-CNN algorithm and for more details user can always read the paper. By the way, we have a 2-minute video here. Problem definition Before we go into details, let us revisit the problem definition of time series anomaly detection. Challenges For any time-series anomaly detection system that is operating in production with a large scale, there are quite a few challenges, especially on the three areas below: 1. Lack of labels - As you can imagine, with signals generated from clients, services, and sensors every second, the huge amount of volume makes it infeasible to manually label the data. 2. Generalization - With real-world data, there are so many different types of time series with different characteristics, which make it hard to generalize and find a silver bullet to solve all the problems. Some examples can be found in the figure below. 3. Efficiency - For any online anomaly detection system, efficiency is one of the key challenges. The system is expected to have low compute cost and low latency for serving. Inspiration In the computer vision domain, there is this concept called “visual saliency detection”. Saliency is what "stands out" in a photo or scene, enabling our eye-brain to quickly focus on the most important regions, as shown in figures below. Fig. Original image Fig. The salient part of the original image When we look at the time series chart, the most dominant and stand-out part is the anomalies. This similarity is where we got the inspiration and it turned out to generate great results. Algorithm Our solution then borrowed Spectral Residual (SR) from the visual saliency detection domain, then apply CNN on the results produced by the SR model As you can see from the algorithm architecture, after SR transformation, the transformed result magnifies the anomalies and the resulting signal is easier to generalize, therefore it provides us a way to training CNN with synthetic data. Spectral Residual The spectral residual algorithm consists of three major steps: Fourier Transform to get the log amplitude spectrum Calculation of spectral residual Inverse Fourier Transform that transforms the sequence back to the spatial domain Benefits SR is unsupervised, efficient, and has good generality. The problem becomes much easier based on the output of the SR model. We can train CNN on the SR output using fully synthetic data with simple synthetic rule Randomly select several points in the saliency map and calculate the injection value to replace the original point. Result We have performed online and offline experimentation, it outperformed state-of-the-arts consistently on open datasets and internal production datasets.
Supply Chain Anomaly Detection and Root Cause Analysis with Azure Metric Advisor
The COVID-19 pandemic has disrupted supply chains globally. Since 2020 there has been a significant increase in online shopping, home improvements, home entertainment and more but supply chains are struggling to keep up with the demand and we are seeing out of stock items, shipping delays, lost items and more. See how Metric Advisor can assist detect anomalies and root cause analysis.
