Del 16255 - Lecture Notes in Computer Science

Advanced Analytics and Learning on Temporal Data

Nyhet

10th ECML PKDD Workshop, AALTD 2025, Porto, Portugal, September 19, 2025, Revised Selected Papers

Häftad, Engelska, 2026

AvVincent Lemaire,Georgiana Ifrim,Anthony Bagnall,Simon Malinowski,Patrick Schäfer,Romain Tavenard

719 kr

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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.

Produktinformation

Utgivningsdatum2026-02-21
Mått155 x 235 x 13 mm
Vikt353 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor215
FörlagSpringer Nature Switzerland AG
ISBN9783032155344

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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.