Anomalies. Oxford dictionary defines them as things that deviate from what is typical or expected. No matter what field you are in, they seem to pop up and occur without warning. In the realm of data, anomalies can lead to incorrect or out-of-date decisions. This means we need to find... Read more

In my most recent default scoring data science projects, I wanted an automatic tool that could warn me, especially during the development stage, when my model’s predictions were incoherent, whether it was because there had been a problem in the data processing, or because the model simply had to be... Read more

A real-world dataset often contains anomalies or outlier data points. The cause of anomalies may be data corruption or experimental or human errors. The presence of anomalies may impact the performance of the model, hence to train a robust data science model, the dataset should be free from anomalies. In... Read more

One of the best parts of data science is that algorithms developed for one application turn up in other applications they were not originally designed for! This is very true in the world of fraud and anomaly detection. Many algorithms have their foundation elsewhere but find their usefulness in detecting... Read more

Anomalies. Oxford dictionary defines them as things that deviate from what is normal or expected. No matter what field you are in, they seem to pop up and occur without warning. In the realm of data, anomalies can lead to incorrect or out-of-date decisions to be made. This means we... Read more

An outlier may be defined as an object that is out of ordinary, which differs significantly from the norm. In day to day examples, it could be a baby panda among adult pandas, a champion breaking a world record, or fraud emails in your inbox. Why even bother to detect... Read more

In this article, I look at data from 135 nights of sleep and use anomaly detection and time series data to understand the results. Three things are certain in life: death, taxes, and sleeping. Here, we’ll talk about the latest. Every night*, us humans, after a long day of roaming... Read more

Overview Assessing and improving readiness remains a significant priority for the United States Army. With this priority in mind, the Army recently launched a project to enhance its supply chain data environments by leveraging the power of artificial intelligence (AI) and machine learning (ML). The Army’s Logistics Innovation Agency (LIA)... Read more

Introduction Inspired by this Netflix post, I decided to write a post based on this topic using R. There are several nice packages to achieve this goal, the one we´re going to review is AnomalyDetection. Download full –and tiny– R code of this post here. Normal Vs. Abnormal The definition for abnormal, or... Read more

It’s time for part 2 of our partner session highlight. Check out more of the talks and workshops from industry-leading data science and AI organizations coming to ODSC East 2023 below. You can see our first round of sessions here. Human-in-the-Loop: Strategies for Improving Time Series Anomaly Detection Andrew Cheesman｜Head... Read more