Abstract: Time series classification requires specialized models that can effectively capture temporal structures. Consequently, Large Language Models (LLMs) have emerged as promising candidates due ...

We propose S-Mamba, a Mamba-based model for time series forecasting, which delegates the extraction of inter-variate correlations and temporal dependencies to a bidirectional Mamba block and a ...

The world tried to kill Andy off but he had to stay alive to to talk about what happened with databases in 2025.

The official code for ["TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)"]. TEMPO is one of the very first open source Time Series Foundation Models for ...

Python gives you far more control, and the ecosystem is stacked with libraries that can replace most no-code platforms if you ...

Abstract: In the field of time series forecasting, time series are often considered as linear time-varying systems, which facilitates the analysis and modeling of time series from a structural state ...