Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
Fuente:
arXiv
Saved in:
| Main Authors: | Liang, Aobo, Sun, Yan, Shi, Xiaohou, Li, Ke |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
by: Liang, Aobo, et al.
Published: (2024)
by: Liang, Aobo, et al.
Published: (2024)
WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting
by: Liang, Aobo, et al.
Published: (2024)
by: Liang, Aobo, et al.
Published: (2024)
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
by: Chen, PengYu, et al.
Published: (2025)
by: Chen, PengYu, et al.
Published: (2025)
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
by: Wu, Shunyu, et al.
Published: (2026)
by: Wu, Shunyu, et al.
Published: (2026)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)
by: Shi, Xiaoming, et al.
Published: (2024)
Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
by: Mahtout, Btissame El, et al.
Published: (2026)
by: Mahtout, Btissame El, et al.
Published: (2026)
Wavelet Mixture of Experts for Time Series Forecasting
by: Zhou, Zheng, et al.
Published: (2025)
by: Zhou, Zheng, et al.
Published: (2025)
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
by: Liu, Yiwen, et al.
Published: (2025)
by: Liu, Yiwen, et al.
Published: (2025)
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
by: Liu, Xu, et al.
Published: (2024)
by: Liu, Xu, et al.
Published: (2024)
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
by: Wang, Rui, et al.
Published: (2026)
by: Wang, Rui, et al.
Published: (2026)
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
by: Ni, Ronghao, et al.
Published: (2023)
by: Ni, Ronghao, et al.
Published: (2023)
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts
by: Liu, Ziqi
Published: (2025)
by: Liu, Ziqi
Published: (2025)
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024)
by: Zhang, Lingzheng, et al.
Published: (2024)
How Foundational are Foundation Models for Time Series Forecasting?
by: Karaouli, Nouha, et al.
Published: (2025)
by: Karaouli, Nouha, et al.
Published: (2025)
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
by: Chen, PengYu, et al.
Published: (2026)
by: Chen, PengYu, et al.
Published: (2026)
AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting
by: Zhang, Huanyao, et al.
Published: (2025)
by: Zhang, Huanyao, et al.
Published: (2025)
TimeExpert: Boosting Long Time Series Forecasting with Temporal Mix of Experts
by: Ma, Xiaowen, et al.
Published: (2025)
by: Ma, Xiaowen, et al.
Published: (2025)
Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
by: Nochumsohn, Liran, et al.
Published: (2025)
by: Nochumsohn, Liran, et al.
Published: (2025)
TimeFound: A Foundation Model for Time Series Forecasting
by: Xiao, Congxi, et al.
Published: (2025)
by: Xiao, Congxi, et al.
Published: (2025)
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
by: Liu, Qingxiang, et al.
Published: (2024)
by: Liu, Qingxiang, et al.
Published: (2024)
MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
by: Ortigossa, Evandro S., et al.
Published: (2026)
by: Ortigossa, Evandro S., et al.
Published: (2026)
Task-Aware Mixture-of-Experts for Time Series Analysis
by: Wu, Xingjian, et al.
Published: (2025)
by: Wu, Xingjian, et al.
Published: (2025)
Time Series Foundation Models for Multivariate Financial Time Series Forecasting
by: Marconi, Ben A.
Published: (2025)
by: Marconi, Ben A.
Published: (2025)
SEMPO: Lightweight Foundation Models for Time Series Forecasting
by: He, Hui, et al.
Published: (2025)
by: He, Hui, et al.
Published: (2025)
Adapting Time Series Foundation Models through Data Mixtures
by: Lee, Thomas L., et al.
Published: (2026)
by: Lee, Thomas L., et al.
Published: (2026)
Distilling Time Series Foundation Models for Efficient Forecasting
by: Li, Yuqi, et al.
Published: (2026)
by: Li, Yuqi, et al.
Published: (2026)
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
by: Lu, Jiecheng, et al.
Published: (2024)
by: Lu, Jiecheng, et al.
Published: (2024)
Reasoning-Aware Training for Time Series Forecasting
by: Ahamed, Md Atik, et al.
Published: (2026)
by: Ahamed, Md Atik, et al.
Published: (2026)
Ada-MoGE: Adaptive Mixture of Gaussian Expert Model for Time Series Forecasting
by: Ni, Zhenliang, et al.
Published: (2025)
by: Ni, Zhenliang, et al.
Published: (2025)
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
by: Li, Hongkai, et al.
Published: (2026)
by: Li, Hongkai, et al.
Published: (2026)
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
by: Li, Yi, et al.
Published: (2026)
by: Li, Yi, et al.
Published: (2026)
Time Series Foundation Models for Process Model Forecasting
by: Yu, Yongbo, et al.
Published: (2025)
by: Yu, Yongbo, et al.
Published: (2025)
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
by: Matos, Ricardo, et al.
Published: (2025)
by: Matos, Ricardo, et al.
Published: (2025)
Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting
by: Pennino, Federico, et al.
Published: (2025)
by: Pennino, Federico, et al.
Published: (2025)
Lightweight Online Adaption for Time Series Foundation Model Forecasts
by: Lee, Thomas L., et al.
Published: (2025)
by: Lee, Thomas L., et al.
Published: (2025)
DAM: Towards A Foundation Model for Time Series Forecasting
by: Darlow, Luke, et al.
Published: (2024)
by: Darlow, Luke, et al.
Published: (2024)
MoDEx: Mixture of Depth-specific Experts for Multivariate Long-term Time Series Forecasting
by: Yoon, Hyekyung, et al.
Published: (2026)
by: Yoon, Hyekyung, et al.
Published: (2026)
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
by: Masserano, Luca, et al.
Published: (2024)
by: Masserano, Luca, et al.
Published: (2024)
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
by: Zhang, Huanyu, et al.
Published: (2024)
by: Zhang, Huanyu, et al.
Published: (2024)
Mixture of Online and Offline Experts for Non-stationary Time Series
by: Zhao, Zhilin, et al.
Published: (2022)
by: Zhao, Zhilin, et al.
Published: (2022)
Similar Items
-
Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
by: Liang, Aobo, et al.
Published: (2024) -
WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting
by: Liang, Aobo, et al.
Published: (2024) -
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
by: Chen, PengYu, et al.
Published: (2025) -
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
by: Wu, Shunyu, et al.
Published: (2026) -
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)