88 citations · 132 across the 13 of their papers we have counts for
10 papers · 1 filter
Moirai 2.0: When Less Is More for Time Series Forecasting
Chenghao Liu, Taha Aksu, Juncheng Liu +7
We introduce Moirai 2.0, a decoder-only time-series foundation model trained on a new corpus of 36M series. The model adopts quantile forecasting and multi-token prediction, improv…
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
Xu Liu, Taha Aksu, Juncheng Liu +7
Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs)…
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…
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…
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
Gerald Woo, Chenghao Liu, Akshat Kumar +1
Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying prog…
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from c…