Priyanga Dilini Talagala

Priyanga Dilini Talagala, Rob J. Hyndman, Kate Smith-Miles

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. Using various synthetic and real datasets, we demonstrate the wide applicability and usefulness of our algorithm, which we call the stray algorithm. We also demonstrate how this algorithm can assist in detecting anomalies present in other data structures using feature engineering. We show the situations where the stray algorithm outperforms the HDoutliers algorithm both in accuracy and computational time. This framework is implemented in the open source R package stray.

Priyanga Dilini Talagala, Thiyanga S. Talagala

Distance education has a long history. However, COVID-19 has created a new era of distance education. Due to the increasing demand, various distance learning solutions have been introduced for different distance education purposes. In this study, we investigated the impact of COVID-19 on global attention towards different distance learning-teaching tools. We used Google Trend search queries as a proxy to quantify the popularity and public interest towards different distance education solutions. Both visual and analytical approaches were used to analyze global-level web search queries during the COVID-19 pandemic. This can provide a fast first step guide to identifying the most popular online learning tools available for different educational purposes. The results allow the teachers to narrow down the search space and deepen their exploration of prominent distance education solutions to support their online teaching. The R code and data to reproduce the results of this work are available in the online supplementary materials.