Transformers for time series forecasting mainly model time series from limited or fixed
scales, making it challenging to capture different characteristics spanning various scales …

The widespread deployment of wireless and mobile devices results in a proliferation of
spatio-temporal data that is used in applications, eg, traffic prediction, human mobility …

Transformer-based forecasting methods have been widely applied to forecast long-term
multivariate time series, which achieves significant improvements on extending the …

Time series are generated in diverse domains such as economic, traffic, health, and energy,
where forecasting of future values has numerous important applications. Not surprisingly …

Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications …

The widespread adoption of scalable mobile sensing has led to large amounts of time series
data for real-world applications. A fundamental application is multivariate time series …

Robust multivariate time series forecasting is crucial in many cyber-physical and Internet of
Things applications. Existing state-of-the-art robust forecasting models decompose time …

We are witnessing an increasing availability of streaming data that may contain valuable
information on the underlying processes. It is thus attractive to be able to deploy machine …

As urbanization accelerates, air quality has become a pressing concern. Accurate air quality
prediction is essential for informed governmental decision-making and for protecting public …

Demystifying interactions between temporal patterns of different scales is fundamental to
precise long-range time series forecasting. However, previous works lack the ability to …