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 …

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

Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In
contrast to conventional methods that involve creating dedicated models for specific time …

Air quality spatiotemporal prediction can provide technical support for environmental
governance and sustainable city development. As a classic multi-source spatiotemporal …

Deep learning-based algorithms, eg, convolutional networks, have significantly facilitated
multivariate time series classification (MTSC) task. Nevertheless, they suffer from 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 …

Time-series and graphs have been extensively studied for their ubiquitous existence in
numerous domains. Both topics have been separately explored in the field of deep learning …

Attention-based models have achieved many remarkable breakthroughs in numerous
applications. However, the quadratic complexity of Attention makes the vanilla …

Power prediction is important as a technical support mechanism in the global carbon
neutrality initiative. Existing artificial intelligence methodologies frequently grapple with the …

Stock trend prediction (STP) aims to predict the price fluctuation, which is critical in financial
trading. The existing STP approaches only use market data with the same granularity (such …