Python has become the go-to language for data analysis, offering powerful libraries for cleaning, exploring, visualizing, and modeling data. From quick exploratory checks to complex predictive ...

Abstract: Most existing outlier detection methods rely on a single and fine-grained data representation, making them vulnerable to noise and inefficient in capturing local anomalies. Granular-ball ...

Abstract: Outlier detection is an effective technique for identifying abnormal samples in complex data. Random walks effectively detect outliers by analyzing graph transition patterns. However, ...

We propose a framework that combines a complex network approach with visibility graph algorithms. First, a comprehensive financial stress index (FSI) for China is constructed by integrating ...

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better. Change point detection is a helpful tool that spots moments when data, such ...

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly ...

Earnings announcements are one of the few scheduled events that consistently move markets. Prices react not just to the reported numbers, but to how those numbers compare with expectations. A small ...

WMOF is an anomaly detection algorithm designed for both batch datasets and real-time data streams. It is an extension of the Mass-ratio-variance Outlier Factor (MOF) algorithm that introduces a ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

The method inputs Doppler observations, satellite positions (from ephemeris), elevation angles, azimuth angles, and C/N₀ values. It groups potential multipath/NLOS faults using elevation, azimuth ...