Cover Image

Mixture-of-Linear-Experts for Long-term Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ni, Ronghao, Lin, Zinan, Wang, Shuaiqi, Fanti, Giulia
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
by: Ortigossa, Evandro S., et al.
Published: (2026)

MoDEx: Mixture of Depth-specific Experts for Multivariate Long-term Time Series Forecasting
by: Yoon, Hyekyung, et al.
Published: (2026)

Improving the Training of Rectified Flows
by: Lee, Sangyun, et al.
Published: (2024)

Wavelet Mixture of Experts for Time Series Forecasting
by: Zhou, Zheng, et al.
Published: (2025)

Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
by: Li, Zhe, et al.
Published: (2023)

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
by: Wang, Rui, et al.
Published: (2026)

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024)

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
by: Liu, Yiwen, et al.
Published: (2025)

SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations
by: Wang, Shuaiqi, et al.
Published: (2026)

To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series
by: Ma, Jiaming, et al.
Published: (2026)

Ada-MoGE: Adaptive Mixture of Gaussian Expert Model for Time Series Forecasting
by: Ni, Zhenliang, et al.
Published: (2025)

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
by: Zhang, Zhenwei, et al.
Published: (2023)

xLSTMTime : Long-term Time Series Forecasting With xLSTM
by: Alharthi, Musleh, et al.
Published: (2024)

Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers
by: Cheng, Xin, et al.
Published: (2024)

WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
by: Wu, Shunyu, et al.
Published: (2026)

Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
by: Ortigossa, Evandro S., et al.
Published: (2026)

Value-Based Pre-Training with Downstream Feedback
by: Ke, Shuqi, et al.
Published: (2026)

MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
by: Cai, Xiuding, et al.
Published: (2024)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
by: Song, Ziyang, et al.
Published: (2023)

Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting
by: Zhao, Yuang, et al.
Published: (2024)

DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
by: Ao, Xiang, et al.
Published: (2026)

M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting
by: Huang, Yaohui, et al.
Published: (2026)

TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting
by: Huang, Songtao, et al.
Published: (2025)

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

Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
by: Zhu, Jiawen, et al.
Published: (2026)

PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
by: Sun, Tian, et al.
Published: (2025)

A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
by: Moges, Henok Tenaw, et al.
Published: (2025)

Multi-Modal Time Series Prediction via Mixture of Modulated Experts
by: Zhang, Lige, et al.
Published: (2026)

DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting
by: Zhu, Mingnan, et al.
Published: (2026)

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
by: Wang, Zheng, et al.
Published: (2025)

CATS-Linear: Classification Auxiliary Linear Model for Time Series Forecasting
by: Jibao, Zipo, et al.
Published: (2025)

Long Input Sequence Network for Long Time Series Forecasting
by: Ma, Chao, et al.
Published: (2024)

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
by: Pan, Licheng, et al.
Published: (2025)

BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
by: Lee, Sangyun, et al.
Published: (2025)

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution
by: Patel, Sara, et al.
Published: (2025)

Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
by: Shen, ChengAo, et al.
Published: (2025)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
by: Zhou, Zhen, et al.
Published: (2026)

Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by: Kim, Byunghyun, et al.
Published: (2024)

vLinear: A Powerful Linear Model for Multivariate Time Series Forecasting
by: Yue, Wenzhen, et al.
Published: (2026)

There are no Champions in Supervised Long-Term Time Series Forecasting
by: Brigato, Lorenzo, et al.
Published: (2025)