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 …

Time series forecasting involves justifying assertions scientifically regarding potential states
or predicting future trends of an event based on historical data recorded at various time …

Multivariate time series forecasting has long received significant attention in real-world
applications, such as energy consumption and traffic prediction. While recent methods …

Unsupervised domain adaptation methods aim at generalizing well on unlabeled test data
that may have a different (shifted) distribution from the training data. Such methods are …

Time series data, including univariate and multivariate ones, are characterized by unique
composition and complex multi-scale temporal variations. They often require special …

Abstract Graph Neural Networks (GNN) have recently gained popularity in the forecasting
domain due to their ability to model complex spatial and temporal patterns in tasks such as …

Multivariate time series (MTS) forecasting is considered as a challenging task due to
complex and nonlinear interdependencies between time steps and series. With the advance …

Accurate and reliable forecasting is a crucial task in many different domains. The selection of
a forecasting algorithm that is suitable for a specific time series can be a challenging task …

Multivariate time series (MTS) prediction has been studied broadly, which is widely applied
in real-world applications. Recently, transformer-based methods have shown the potential in …

Solar panels are exposed to various pollutants in outdoor environments, such as dust,
sediment, and bird excrement, which can cause the power generated by the panels to drop …