The field of time series forecasting has garnered significant attention in recent years,
prompting the development of advanced models like TimeSieve, which demonstrates …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

Foundation models, especially LLMs, are profoundly transforming deep learning. Instead of
training many task-specific models, we can adapt a single pretrained model to many tasks …

Explaining multivariate time series is a compound challenge, as it requires identifying
important locations in the time series and matching complex temporal patterns. Although …

Explaining deep learning models operating on time series data is crucial in various
applications of interest which require interpretable and transparent insights from time series …

Long-term forecasting for time series is gaining significant attention in many emerging fields,
such as machine learning and artificial intelligence. Linear fuzzy information granulation is a …

We propose a data-driven and machine-learning-based approach to compute non-Galerkin
coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of …

Recent research has developed a number of eXplainable AI (XAI) techniques. Although
extracting meaningful insights from deep learning models, how to properly evaluate these …

Interpreting complex time series forecasting models is challenging due to the temporal
dependencies between time steps and the dynamic relevance of input features over time …

State-of-the-art methods for explaining predictions based on time series are built on learning
an instance-wise saliency mask for each time step. However, for many types of time series …