EffiCANet: Efficient Time Series Forecasting with Convolutional Attention
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhou, Xinxing, Ye, Jiaqi, Zhao, Shubao, Jin, Ming, Yang, Chengyi, Wen, Yanlong, Yuan, Xiaojie |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
por: Zhou, Xinxing, et al.
Publicado: (2024)
por: Zhou, Xinxing, et al.
Publicado: (2024)
EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
por: Zhou, Xinxing, et al.
Publicado: (2026)
por: Zhou, Xinxing, et al.
Publicado: (2026)
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
por: Wang, Yanlong, et al.
Publicado: (2024)
por: Wang, Yanlong, et al.
Publicado: (2024)
Attention as Robust Representation for Time Series Forecasting
por: Niu, PeiSong, et al.
Publicado: (2024)
por: Niu, PeiSong, et al.
Publicado: (2024)
Accurate and Efficient Multi-Channel Time Series Forecasting via Sparse Attention Mechanism
por: Gao, Lei, et al.
Publicado: (2026)
por: Gao, Lei, et al.
Publicado: (2026)
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting
por: Zhao, Shubao, et al.
Publicado: (2024)
por: Zhao, Shubao, et al.
Publicado: (2024)
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
por: Jin, Ming, et al.
Publicado: (2023)
por: Jin, Ming, et al.
Publicado: (2023)
Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
por: Cao, Fanpu, et al.
Publicado: (2024)
por: Cao, Fanpu, et al.
Publicado: (2024)
Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
por: Li, Feifei, et al.
Publicado: (2024)
por: Li, Feifei, et al.
Publicado: (2024)
EffiFusion-GAN: Efficient Fusion Generative Adversarial Network for Speech Enhancement
por: Wen, Bin, et al.
Publicado: (2025)
por: Wen, Bin, et al.
Publicado: (2025)
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
por: Jin, Ming, et al.
Publicado: (2023)
por: Jin, Ming, et al.
Publicado: (2023)
StretchTime: Adaptive Time Series Forecasting via Symplectic Attention
por: Kim, Yubin, et al.
Publicado: (2026)
por: Kim, Yubin, et al.
Publicado: (2026)
Revisiting Attention for Multivariate Time Series Forecasting
por: Wu, Haixiang
Publicado: (2024)
por: Wu, Haixiang
Publicado: (2024)
Are Self-Attentions Effective for Time Series Forecasting?
por: Kim, Dongbin, et al.
Publicado: (2024)
por: Kim, Dongbin, et al.
Publicado: (2024)
Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting
por: Zhao, Yuang, et al.
Publicado: (2024)
por: Zhao, Yuang, et al.
Publicado: (2024)
FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting
por: Li, Ruiqi, et al.
Publicado: (2024)
por: Li, Ruiqi, et al.
Publicado: (2024)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
por: Shi, Xiaoming, et al.
Publicado: (2024)
por: Shi, Xiaoming, et al.
Publicado: (2024)
EventTSF: Event-Aware Non-Stationary Time Series Forecasting
por: Ge, Yunfeng, et al.
Publicado: (2025)
por: Ge, Yunfeng, et al.
Publicado: (2025)
PeriodNet: Boosting the Potential of Attention Mechanism for Time Series Forecasting
por: Zhao, Bowen, et al.
Publicado: (2025)
por: Zhao, Bowen, et al.
Publicado: (2025)
TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting
por: Ankireddy, Sravan Kumar, et al.
Publicado: (2026)
por: Ankireddy, Sravan Kumar, et al.
Publicado: (2026)
Distilling Time Series Foundation Models for Efficient Forecasting
por: Li, Yuqi, et al.
Publicado: (2026)
por: Li, Yuqi, et al.
Publicado: (2026)
Wavelet Mixture of Experts for Time Series Forecasting
por: Zhou, Zheng, et al.
Publicado: (2025)
por: Zhou, Zheng, et al.
Publicado: (2025)
Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
por: Tang, Dahao, et al.
Publicado: (2025)
por: Tang, Dahao, et al.
Publicado: (2025)
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
por: Yang, Silin, et al.
Publicado: (2024)
por: Yang, Silin, et al.
Publicado: (2024)
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
por: Zhang, Weijia, et al.
Publicado: (2023)
por: Zhang, Weijia, et al.
Publicado: (2023)
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
por: Pan, Sheng, et al.
Publicado: (2026)
por: Pan, Sheng, et al.
Publicado: (2026)
SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting
por: Tang, Kaiwen, et al.
Publicado: (2026)
por: Tang, Kaiwen, et al.
Publicado: (2026)
CAPS: Unifying Attention, Recurrence, and Alignment in Transformer-based Time Series Forecasting
por: Pati, Viresh, et al.
Publicado: (2026)
por: Pati, Viresh, et al.
Publicado: (2026)
Nested Spatio-Temporal Time Series Forecasting
por: Ai, Yinghao, et al.
Publicado: (2026)
por: Ai, Yinghao, et al.
Publicado: (2026)
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
por: Hu, Jiaxi, et al.
Publicado: (2024)
por: Hu, Jiaxi, et al.
Publicado: (2024)
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
por: Hu, Jiaxi, et al.
Publicado: (2024)
por: Hu, Jiaxi, et al.
Publicado: (2024)
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
por: Ke, Yekun, et al.
Publicado: (2024)
por: Ke, Yekun, et al.
Publicado: (2024)
A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting
por: Wan, Cheng, et al.
Publicado: (2024)
por: Wan, Cheng, et al.
Publicado: (2024)
Performative Time-Series Forecasting
por: Zhao, Zhiyuan, et al.
Publicado: (2023)
por: Zhao, Zhiyuan, et al.
Publicado: (2023)
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
por: Niu, Yiming, et al.
Publicado: (2025)
por: Niu, Yiming, et al.
Publicado: (2025)
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
por: Hu, Yifan, et al.
Publicado: (2025)
por: Hu, Yifan, et al.
Publicado: (2025)
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting
por: Ouyang, Pengpeng, et al.
Publicado: (2025)
por: Ouyang, Pengpeng, et al.
Publicado: (2025)
Correlating Time Series with Interpretable Convolutional Kernels
por: Chen, Xinyu, et al.
Publicado: (2024)
por: Chen, Xinyu, et al.
Publicado: (2024)
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
por: Lee, Gawon, et al.
Publicado: (2025)
por: Lee, Gawon, et al.
Publicado: (2025)
Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
por: Wang, Hao, et al.
Publicado: (2025)
por: Wang, Hao, et al.
Publicado: (2025)
Ejemplares similares
-
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
por: Zhou, Xinxing, et al.
Publicado: (2024) -
EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
por: Zhou, Xinxing, et al.
Publicado: (2026) -
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
por: Wang, Yanlong, et al.
Publicado: (2024) -
Attention as Robust Representation for Time Series Forecasting
por: Niu, PeiSong, et al.
Publicado: (2024) -
Accurate and Efficient Multi-Channel Time Series Forecasting via Sparse Attention Mechanism
por: Gao, Lei, et al.
Publicado: (2026)