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
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
Daria Bystrova, Charles K. Assaad, Julyan Arbel, Emilie Devijver, Eric Gaussier, Wilfried Thuiller
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li
Feature Programming for Multivariate Time Series Prediction
Alex Reneau, Jerry Yao-Chieh Hu, Chenwei Xu, Weijian Li, Ammar Gilani, Han Liu
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Jingquan Yan, Hao Wang
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue