Koopman neural operator forecaster for time-series with temporal distributional shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur
in real-world time series and pose a fundamental challenge for deep neural networks …
in real-world time series and pose a fundamental challenge for deep neural networks …
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
Y Li, J Xu, D Anastasiu - Proceedings of the AAAI Conference on …, 2024 - ojs.aaai.org
In the hydrology field, time series forecasting is crucial for efficient water resource
management, improving flood and drought control and increasing the safety and quality of …
management, improving flood and drought control and increasing the safety and quality of …
Universal time-series representation learning: A survey
Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …
satellites in the sky to wearable devices on human bodies. Learning representations by …
Koopman neural forecaster for time series with temporal distribution shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur
in real-world time series and pose a fundamental challenge for deep neural networks …
in real-world time series and pose a fundamental challenge for deep neural networks …
Sample and predict your latent: modality-free sequential disentanglement via contrastive estimation
Unsupervised disentanglement is a long-standing challenge in representation learning.
Recently, self-supervised techniques achieved impressive results in the sequential setting …
Recently, self-supervised techniques achieved impressive results in the sequential setting …
Sequential multi-dimensional self-supervised learning for clinical time series
Self-supervised learning (SSL) for clinical time series data has received significant attention
in recent literature, since these data are highly rich and provide important information about …
in recent literature, since these data are highly rich and provide important information about …
On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness
Multimodal spatiotemporal data (MST) consists of multiple simultaneous spatiotemporal
modalities that interact with each other in a dynamic manner. Due to the complexity of MST …
modalities that interact with each other in a dynamic manner. Due to the complexity of MST …
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Due to their unsupervised training and uncertainty estimation, deep Variational
Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series …
Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series …
A Global View-Guided Autoregressive Residual Network for Irregular Time Series Classification
Irregularly sampled multivariate time series classification tasks become prevalent due to
widespread application of sensors. However, different collection frequencies or sensor …
widespread application of sensors. However, different collection frequencies or sensor …
Sequential Disentanglement by Extracting Static Information From A Single Sequence Element
One of the fundamental representation learning tasks is unsupervised sequential
disentanglement, where latent codes of inputs are decomposed to a single static factor and …
disentanglement, where latent codes of inputs are decomposed to a single static factor and …