Cover Image

Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Xu, Liu, Juncheng, Woo, Gerald, Aksu, Taha, Liang, Yuxuan, Zimmermann, Roger, Liu, Chenghao, Savarese, Silvio, Xiong, Caiming, Sahoo, Doyen
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Moirai 2.0: When Less Is More for Time Series Forecasting
by: Liu, Chenghao, et al.
Published: (2025)

GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
by: Aksu, Taha, et al.
Published: (2024)

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
by: Liu, Xu, et al.
Published: (2025)

Unified Training of Universal Time Series Forecasting Transformers
by: Woo, Gerald, et al.
Published: (2024)

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024)

XForecast: Evaluating Natural Language Explanations for Time Series Forecasting
by: Aksu, Taha, et al.
Published: (2024)

INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness
by: Le, Hung, et al.
Published: (2024)

CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models
by: Li, Jierui, et al.
Published: (2024)

PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback
by: Peng, Yun, et al.
Published: (2024)

Automatic Curriculum Expert Iteration for Reliable LLM Reasoning
by: Zhao, Zirui, et al.
Published: (2024)

Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
by: Liu, Qingxiang, et al.
Published: (2024)

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)

Reward-Guided Speculative Decoding for Efficient LLM Reasoning
by: Liao, Baohao, et al.
Published: (2025)

Entropy-Based Block Pruning for Efficient Large Language Models
by: Yang, Liangwei, et al.
Published: (2025)

ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)

SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)

Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts
by: Zhang, Yue, et al.
Published: (2025)

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
by: Wang, Peiran, et al.
Published: (2025)

Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series
by: Cádiz-Leyton, Martina, et al.
Published: (2025)

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)

S2MoE: Robust Sparse Mixture of Experts via Stochastic Learning
by: Do, Giang, et al.
Published: (2025)

MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Language-Conditioned Mixture-of-Experts Transformers
by: Guo, Kangjun, et al.
Published: (2026)

$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
by: Liu, Zongfang, et al.
Published: (2026)

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
by: Feng, Jiarui, et al.
Published: (2026)

MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
by: Jiang, Yinsicheng, et al.
Published: (2025)

MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
by: Jiang, Yinsicheng, et al.
Published: (2024)

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)

Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
by: Lan, Tian, et al.
Published: (2024)

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
by: Luo, Ziyang, et al.
Published: (2025)

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)

LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)

MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)

MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)

$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
by: Guo, Xiaohan, et al.
Published: (2025)