Advanced Analytics and Learning on Temporal Data

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.

• Erscheinungsdatum: 20.02.2026
• Autor/Autorin: Vincent Lemaire
• Reihe: Springer Nature Proceedings Computer Science
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 16 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 215 Seiten
• ISBN: 9783032155351
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
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