IEEE

Long-Term Time Series Forecasting With Multilinear Trend Fuzzy Information Granules for LSTM in a Periodic Framework

Abstract: Considerable research achievements have been made in utilizing information granulation as an effective technique for addressing long-term time-series forecasting. However, existing studies ...
GitHub

Is Mamba Effective for Time Series Forecasting?

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 ...
GitHub

Time Series Foundation Model - TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

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 ...
IEEE

Kalman-SSM: Modeling Long-Term Time Series With Kalman Filter Structured State Spaces

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 ...
japannews.yomiuri

Japan to Have No Pandas for 1st Time Since 1972 with Return of Ueno Zoo’s Xiao Xiao and Lei Lei to China (Update 1)

Twin giant pandas Xiao Xiao and Lei Lei at Ueno Zoological Gardens in Taito Ward, Tokyo, will be returned to China in late January 2026, the Tokyo metropolitan government and the Tokyo Zoological Park ...