Advanced Analytics and Learning on Temporal Data: 10th ECML PKDD Workshop, AALTD 2025, Porto, Portugal, September 19, 2025, Revised Selected Papers

Author:   Vincent Lemaire ,  Georgiana Ifrim ,  Anthony Bagnall ,  Simon Malinowski
Publisher:   Springer Nature Switzerland AG
ISBN:  

9783032155344


Pages:   210
Publication Date:   04 February 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
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Advanced Analytics and Learning on Temporal Data: 10th ECML PKDD Workshop, AALTD 2025, Porto, Portugal, September 19, 2025, Revised Selected Papers


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Author:   Vincent Lemaire ,  Georgiana Ifrim ,  Anthony Bagnall ,  Simon Malinowski
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032155344


ISBN 10:   3032155347
Pages:   210
Publication Date:   04 February 2026
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

e-SMOTE: a train set rebalancing algorithm for time series classification.- The Next Motif: Tapping into Recurrence Dynamics and Precursor Signals to Forecast Events of Interest.- Re-framing Time Series Augmentation Through the Lens of Generative Models.- FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption.- MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling.- A Deep Dive into Alternatives to the Global Average Pooling for Time Series Classification.- Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting.- Unsupervised Feature Construction for Time Series Anomaly Detection - An Evaluation.- Multi-output Ensembles for Multi-step Forecasting.- Time series extrinsic regression algorithms for forecasting long time series with a short horizon.- Towards a Library for the Analysis of Temporal Sequences.- FiTEM: Fine-tuning Time-series Foundation Models for Selective Forecasting.- T3A-LLM: A Two-Stage Temporal Knowledge Graph Alignment Method Enhanced by LLM.

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