Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

A variety of real-world applications rely on far future information to make decisions, thus
calling for efficient and accurate long sequence multivariate time series forecasting. While …

Time series forecasting is essential for a wide range of real-world applications. Recent
studies have shown the superiority of Transformer in dealing with such problems, especially …

Long series time forecasting has become a popular research direction in recent years, due
to the ability to predict weather changes, traffic conditions and so on. This paper provides a …

Traffic prediction is an important component of the intelligent transportation system. Existing
deep learning methods encode temporal information and spatial information separately or …

Many real-world applications show growing demand for the prediction of long sequence
time-series, such as electricity consumption planning. Long sequence time-series …

Although current time-series forecasting methods have significantly improved the state-of-
the-art (SOTA) results for long-sequence time-series forecasting (LSTF), they still have …

Time series data is ubiquitous in research as well as in a wide variety of industrial
applications. Effectively analyzing the available historical data and providing insights into …

Structured distributions, ie distributions over combinatorial spaces, are commonly used to
learn latent probabilistic representations from observed data. However, scaling these …

For many fields in real life, time series forecasting is essential. Recent studies have shown
that Transformer has certain advantages when dealing with such problems, especially when …