The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

Real-world time series are characterized by intrinsic non-stationarity that poses a principal
challenge for deep forecasting models. While previous models suffer from complicated …

Real-world time-series datasets are often multivariate with complex dynamics. To capture
this complexity, high capacity architectures like recurrent-or attention-based sequential deep …

Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …

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 …

Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Multivariate time series long-term prediction, which aims to predict the change of data in a
long time, can provide references for decision-making. Although transformer-based models …