Saved in:
| Main Authors: | Matos, Ricardo, Roque, Luis, Cerqueira, Vitor |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07490 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting
by: Kasprzyk, Mateusz, et al.
Published: (2024)
by: Kasprzyk, Mateusz, et al.
Published: (2024)
Feature-aligned N-BEATS with Sinkhorn divergence
by: Lee, Joonhun, et al.
Published: (2023)
by: Lee, Joonhun, et al.
Published: (2023)
Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series
by: Sorensen, Daniel, et al.
Published: (2025)
by: Sorensen, Daniel, et al.
Published: (2025)
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025)
by: Cerqueira, Vitor, et al.
Published: (2025)
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
by: Roque, Luis, et al.
Published: (2024)
by: Roque, Luis, et al.
Published: (2024)
L-GTA: Latent Generative Modeling for Time Series Augmentation
by: Roque, Luis, et al.
Published: (2025)
by: Roque, Luis, et al.
Published: (2025)
Online Data Augmentation for Forecasting with Deep Learning
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
BEATS, DREAMS AND SHADOWS
by: Diego Juárez
Published: (2008)
by: Diego Juárez
Published: (2008)
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024)
by: Leites, José, et al.
Published: (2024)
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
by: Sun, Linzhuang, et al.
Published: (2024)
by: Sun, Linzhuang, et al.
Published: (2024)
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
by: Cerqueira, Vitor, et al.
Published: (2022)
by: Cerqueira, Vitor, et al.
Published: (2022)
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)
Time Series Data Augmentation as an Imbalanced Learning Problem
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS
by: Apte, Mohit, et al.
Published: (2024)
by: Apte, Mohit, et al.
Published: (2024)
Wavelet Mixture of Experts for Time Series Forecasting
by: Zhou, Zheng, et al.
Published: (2025)
by: Zhou, Zheng, et al.
Published: (2025)
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
by: Feng, Yu, et al.
Published: (2025)
by: Feng, Yu, et al.
Published: (2025)
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)
Grasynda: Graph-based Synthetic Time Series Generation
by: Amorim, Luis, et al.
Published: (2026)
by: Amorim, Luis, et al.
Published: (2026)
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)
Meta-learning and Data Augmentation for Stress Testing Forecasting Models
by: Inácio, Ricardo, et al.
Published: (2024)
by: Inácio, Ricardo, et al.
Published: (2024)
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)
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)
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
by: Liang, Aobo, et al.
Published: (2025)
by: Liang, Aobo, et al.
Published: (2025)
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts
by: Liu, Ziqi
Published: (2025)
by: Liu, Ziqi
Published: (2025)
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)
Task-Aware Mixture-of-Experts for Time Series Analysis
by: Wu, Xingjian, et al.
Published: (2025)
by: Wu, Xingjian, et al.
Published: (2025)
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)
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)
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)
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
by: Roque, Luis, et al.
Published: (2024)
by: Roque, Luis, 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)
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)
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)
Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
by: Ortigossa, Evandro S., et al.
Published: (2026)
by: Ortigossa, Evandro S., 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)
Hyperparameter Transfer with Mixture-of-Expert Layers
by: Jiang, Tianze, et al.
Published: (2026)
by: Jiang, Tianze, et al.
Published: (2026)
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
by: Lin, Jiafeng, et al.
Published: (2026)
by: Lin, Jiafeng, et al.
Published: (2026)
Dynamic Multi-period Experts for Online Time Series Forecasting
by: Hong, Seungha, et al.
Published: (2026)
by: Hong, Seungha, et al.
Published: (2026)
Similar Items
-
Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting
by: Kasprzyk, Mateusz, et al.
Published: (2024) -
Feature-aligned N-BEATS with Sinkhorn divergence
by: Lee, Joonhun, et al.
Published: (2023) -
Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series
by: Sorensen, Daniel, et al.
Published: (2025) -
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025) -
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
by: Cerqueira, Vitor, et al.
Published: (2024)