Advanced Analytics and Learning on Temporal Data 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers

This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Da...

Full description

Bibliographic Details
Other Authors: Ifrim, Georgiana (Editor), Tavenard, Romain (Editor), Bagnall, Anthony (Editor), Schaefer, Patrick (Editor)
Format: eBook
Language:English
Published: Cham Springer Nature Switzerland 2023, 2023
Edition:1st ed. 2023
Series:Lecture Notes in Artificial Intelligence
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 03436nmm a2200313 u 4500
001 EB002190232
003 EBX01000000000000001327697
005 00000000000000.0
007 cr|||||||||||||||||||||
008 240104 ||| eng
020 |a 9783031498961 
100 1 |a Ifrim, Georgiana  |e [editor] 
245 0 0 |a Advanced Analytics and Learning on Temporal Data  |h Elektronische Ressource  |b 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers  |c edited by Georgiana Ifrim, Romain Tavenard, Anthony Bagnall, Patrick Schaefer, Simon Malinowski, Thomas Guyet, Vincent Lemaire 
250 |a 1st ed. 2023 
260 |a Cham  |b Springer Nature Switzerland  |c 2023, 2023 
300 |a XIII, 308 p. 113 illus., 90 illus. in color  |b online resource 
505 0 |a 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 Series Classification -- 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 
653 |a Artificial Intelligence 
653 |a Artificial intelligence 
700 1 |a Tavenard, Romain  |e [editor] 
700 1 |a Bagnall, Anthony  |e [editor] 
700 1 |a Schaefer, Patrick  |e [editor] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Lecture Notes in Artificial Intelligence 
028 5 0 |a 10.1007/978-3-031-49896-1 
856 4 0 |u https://doi.org/10.1007/978-3-031-49896-1?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006.3 
520 |a This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.