88 citations · 132 across the 13 of their papers we have counts for
7 papers · 1 filter
Incremental Label Distribution Learning with Scalable Graph Convolutional Networks
Ziqi Jia, Xiaoyang Qu, Chenghao Liu +1
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes…
GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
Taha Aksu, Gerald Woo, Juncheng Liu +5
Time series foundation models excel in zero-shot forecasting, handling diverse tasks without explicit training. However, the advancement of these models has been hindered by the la…
XForecast: Evaluating Natural Language Explanations for Time Series Forecasting
Taha Aksu, Chenghao Liu, Amrita Saha +3
Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions, making it very important to understand and explain these models to ensur…
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Xu Liu, Juncheng Liu, Gerald Woo +7
Time series foundation models have demonstrated impressive performance as zero-shot forecasters. However, achieving effectively unified training on time series remains an open chal…
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
Juncheng Liu, Chenghao Liu, Gerald Woo +4
Transformer-based models have emerged as powerful tools for multivariate time series forecasting (MTSF). However, existing Transformer models often fall short of capturing both int…
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
Qingxiang Liu, Xu Liu, Chenghao Liu +2
Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity. While recent efforts…
Unified Training of Universal Time Series Forecasting Transformers
Gerald Woo, Chenghao Liu, Akshat Kumar +3
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large…