Time Series
Time series analysis focuses on understanding and modeling data points collected over time, aiming to extract patterns, make predictions, and gain insights from sequential information. Current research emphasizes developing advanced model architectures, such as transformers and recurrent neural networks (RNNs/LSTMs), to handle increasingly complex, high-dimensional, and non-stationary time series data, often incorporating techniques like attention mechanisms and mixture-of-experts models for improved efficiency and accuracy. This field is crucial for numerous applications across diverse domains, including finance, healthcare, and environmental monitoring, enabling better forecasting, anomaly detection, and decision-making based on temporal data.
Papers
On the balance between the training time and interpretability of neural ODE for time series modelling
Yakov Golovanev, Alexander Hvatov
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)
Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Kashif Rasul, Young-Jin Park, Max Nihlén Ramström, Kyung-Min Kim
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Iris A. M. Huijben, Arthur A. Nijdam, Sebastiaan Overeem, Merel M. van Gilst, Ruud J. G. van Sloun
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
Mostafa Rahmani, Anoop Deoras, Laurent Callot