Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …

Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …

Given the ubiquitous presence of time series data across various domains, precise
forecasting of time series holds significant importance and finds widespread real-world …

Abstract Electronic Health Records (EHRs) significantly enhance clinical decision-making,
particularly in safe and effective medication recommendation based on complex patient …

While most time series are non-stationary, it is inevitable for models to face the distribution
shift issue in time series forecasting. Existing solutions manipulate statistical measures …

Long-term time series forecasting is a long-standing challenge in various applications. A
central issue in time series forecasting is that methods should expressively capture long …

Fourier analysis has been an instrumental tool in the development of signal processing. This
leads us to wonder whether this framework could similarly benefit generative modelling. In …

Due to non-stationarity of time series, the distribution shift problem largely hinders the
performance of time series forecasting. Existing solutions either fail for the shifts beyond …

The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the
challenging task of fMRI-to-image retrieval. State-of-the-art MindEye remarkably improves …