Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

While numerous forecasters have been proposed using different network architectures, the
Transformer-based models have state-of-the-art performance in time series forecasting …

Time series forecasting typically needs to address non-stationary data with evolving trend
and seasonal patterns. To address the non-stationarity, reversible instance normalization …

Training models on spatio-temporal (ST) data poses an open problem due to the
complicated and diverse nature of the data itself, and it is challenging to ensure the model's …

Non-stationarity poses significant challenges for multivariate time series forecasting due to
the inherent short-term fluctuations and long-term trends that can lead to spurious …

Abstract Long-term Time Series Forecasting (LTSF) plays a crucial role in real-world
applications for early warning and decision-making. Time series inherently embody complex …

This paper presents\textbf {FreEformer}, a simple yet effective model that leverages a\textbf
{Fre} quency\textbf {E} nhanced Trans\textbf {former} for multivariate time series forecasting …

The non-stationary nature of real-world Multivariate Time Series (MTS) data presents
forecasting models with a formidable challenge of the time-variant distribution of time series …