End-To-End Self-tuning Self-supervised Time Series Anomaly Detection

no code implementations • 3 Apr 2024 • Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu

A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)

Anomaly Detection Data Augmentation +2

Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors

no code implementations • 9 May 2023 • Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral Asensio

IoT data is a central element in the successful digital transformation of agriculture.

Dynamic Time Warping Self-Supervised Anomaly Detection +1