E-Book, Englisch, Band 14343, 308 Seiten, eBook
8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers
E-Book, Englisch, Band 14343, 308 Seiten, eBook
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-49896-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Human Activity Segmentation Challenge.- Human Activity Segmentation Challenge@ECML/PKDD’23.- Change points detection in multivariate signal applied to human activity segmentation.- Change Point Detection via Synthetic Signals.- Oral Presentation.- Clustering time series with k-medoids based algorithms.- Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting.- RED CoMETS: an ensemble classifier for symbolically represented multivariate time series.- Deep Long Term Prediction for Semantic Segmentation in Autonomous Driving.- Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression.- ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging.- Poster Presentation.- Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks.- Evaluating Explanation Methods for Multivariate Time SeriesClassification.- tGLAD: A sparse graph recovery based approach for multivariate time series segmentation.- Designing a New Search Space for Multivariate Time-Series Neural Architecture Search.- Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms.- Do Cows Have Fingerprints? Using Time Series Techniques and Milk Flow Profiles to Characterise Cow Behaviours and Detect Health Issues.- Exploiting Context and Attention with Recurrent Neural Network for Sensor Time Series Prediction.- Rail Crack Propagation Forecasting Using Multi-horizons RNNs.- Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies.- Time-aware Predictions of Moments of Change in Longitudinal User Posts on Social Media.