Advanced Analytics and Learning on Temporal Data
Springer International Publishing (Verlag)
978-3-032-15534-4 (ISBN)
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This book constitutes the revised selected papers of the 10th ECML PKDD workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2025, held in Porto, Portugal, on September 19, 2025.
The 13 full papers presented here were carefully reviewed and selected from 22 submissions. The papers focus on the following topics: Clustering and Analysis of Time Series; Forecasting and Prediction; Analysis and Processing of Time Series; Models and Approaches Based on Deep Learning and LLMs; Time Series Classification; Augmentation, Imputation, and Preprocessing Techniques.
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.
| Erscheint lt. Verlag | 15.3.2026 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XXV, 210 p. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | batch learning • Boosting • classification and regression trees • Computing Methodologies • Data Mining • Data streaming • Forecasting • information systems • Instance-Based Learning • Mathematics of Computing • spatial-temporal systems • Supervised learning by classification • Time Series Analysis |
| ISBN-10 | 3-032-15534-7 / 3032155347 |
| ISBN-13 | 978-3-032-15534-4 / 9783032155344 |
| Zustand | Neuware |
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