Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

In the post-deep learning era, the Transformer architecture has demonstrated its powerful
performance across pre-trained big models and various downstream tasks. However, the …

The analysis of tabular data has traditionally been dominated by gradient-boosted decision
trees (GBDTs), known for their proficiency with mixed categorical and numerical features …

In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the
input sequences is crucial. While popular Transformer-based predictive models can perform …

Traditional recurrent neural network architectures, such as long short-term memory neural
networks (LSTM), have historically held a prominent role in time series forecasting (TSF) …

This paper introduces SICSM, a novel structural inference framework that integrates
Selective State Space Models (selective SSMs) with Generative Flow Networks (GFNs) to …

Temporal dynamic graphs (TDGs), representing the dynamic evolution of entities and their
relationships over time with intricate temporal features, are widely used in various real-world …

This paper introduces Mamba4Cast, a zero-shot foundation model for time series
forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks …

Transformer-based methods have achieved excellent results in the field of time series
forecasting due to their powerful ability to model sequences and capture their long-term …

Time series forecasting has been a challenging area in the field of Artificial Intelligence.
Various approaches such as linear neural networks, recurrent linear neural networks …