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Found 5 papers, 1 papers with code
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data.
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone
The selection is based on the user's preferences regarding anomaly detection accuracy and computational cost.
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations
We showed that for similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1. 8x on average; however, rates varied between 1x and 18x depending on the dataset.
A General Model of Conversational Dynamics and an Example Application in Serious Illness Communication
In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations.
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering
Additionally, using available meteorological data associated with storm events, we examine the characteristics of computational clusters of storm events in the study watersheds and identify the features driving the clustering approach.
