Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models

Kalyan Veeramachaneni and his team at the MIT Data-to-AI (DAI) Lab have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. They’ve been building it for three years—AER has been learning and extracting intelligence for signals and has reached maturity to outperform the market’s leading models significantly.

Anomaly Detection: Its Real-Life Uses and the Latest Advances

In this contributed article, Al Gharakhanian, Machine Learning Development Director, Cognityze, takes a look at anomaly detection in terms of real-life use cases, addressing critical factors, along with the relationship with machine learning and artificial neural networks.

Research Highlights: MIDAS – Real-time Anomaly/Fake News/Intrusion Detection

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review MIDAS – Real-time Anomaly/Fake News/Intrusion Detection developed by Ph.D. candidate Siddharth Bhatia and his team at the National University of Singapore.

Act Early to Embrace 5G: Accelerate Returns Via Anomaly Detection

In this contributed article, Pratap Dangeti, Principal Data Scientist at CrunchMetrics, discusses anomaly detection on 5G and how it’s useful to leverage a combination of statistical methods and AI and ML-based algorithms to detect anomalies in your data and alert you in real time – so that you can take preventive action to avert business-critical issues and leverage profit-generating opportunities.

Detecting Anomalies in Time Series Data: Deciphering the Noise and Zoning in on the Signals

In this contributed article, Pratap Dangeti, Principal Data Scientist at Subex, discusses how anomaly detection in industrial data is by no means a simple process given the scale at which it needs to happen, and also the highly dynamic nature of business in today’s world. However, it’s still imperative to get it right, as no digital business can hope to stay relevant and competitive in an increasingly tough economy without the power of meaningful data analytics to back its growth.