Selective Learning for Deep Time Series Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | Fu, Yisong, Shao, Zezhi, Yu, Chengqing, Li, Yujie, An, Zhulin, Wang, Qi, Xu, Yongjun, Wang, Fei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting
by: Wang, Fei, et al.
Published: (2025)
by: Wang, Fei, et al.
Published: (2025)
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
by: Li, Yujie, et al.
Published: (2025)
by: Li, Yujie, et al.
Published: (2025)
On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting
by: Fu, Yisong, et al.
Published: (2024)
by: Fu, Yisong, et al.
Published: (2024)
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models
by: Shao, Zezhi, et al.
Published: (2025)
by: Shao, Zezhi, et al.
Published: (2025)
Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates
by: Yu, Chengqing, et al.
Published: (2025)
by: Yu, Chengqing, et al.
Published: (2025)
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
by: Yu, Chengqing, et al.
Published: (2024)
by: Yu, Chengqing, et al.
Published: (2024)
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
by: Shao, Zezhi, et al.
Published: (2023)
by: Shao, Zezhi, et al.
Published: (2023)
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
by: Shao, Zezhi, et al.
Published: (2023)
by: Shao, Zezhi, et al.
Published: (2023)
STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach
by: Li, Yujie, et al.
Published: (2025)
by: Li, Yujie, et al.
Published: (2025)
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
by: Liu, Yangyou, et al.
Published: (2026)
by: Liu, Yangyou, et al.
Published: (2026)
Online Policy Distillation with Decision-Attention
by: Yu, Xinqiang, et al.
Published: (2024)
by: Yu, Xinqiang, et al.
Published: (2024)
From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoning
by: Ni, Hang, et al.
Published: (2026)
by: Ni, Hang, et al.
Published: (2026)
From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
by: Liu, Ruiqi, et al.
Published: (2026)
by: Liu, Ruiqi, et al.
Published: (2026)
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
by: Talukder, Sabera, et al.
Published: (2024)
by: Talukder, Sabera, et al.
Published: (2024)
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)
TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
by: Liang, Shuang, et al.
Published: (2025)
by: Liang, Shuang, et al.
Published: (2025)
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
by: Wang, Yanlong, et al.
Published: (2024)
by: Wang, Yanlong, et al.
Published: (2024)
Low-redundancy Distillation for Continual Learning
by: Liu, RuiQi, et al.
Published: (2023)
by: Liu, RuiQi, et al.
Published: (2023)
Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
by: Wang, Hao, et al.
Published: (2026)
by: Wang, Hao, et al.
Published: (2026)
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
by: Fan, Wei, et al.
Published: (2024)
by: Fan, Wei, et al.
Published: (2024)
The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
by: Shen, Lefei, et al.
Published: (2025)
by: Shen, Lefei, et al.
Published: (2025)
HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
by: Wang, Tan, et al.
Published: (2025)
by: Wang, Tan, et al.
Published: (2025)
Multi-period Learning for Financial Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2025)
by: Zhang, Xu, et al.
Published: (2025)
Graph Deep Learning for Time Series Forecasting
by: Cini, Andrea, et al.
Published: (2023)
by: Cini, Andrea, et al.
Published: (2023)
Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
by: Wang, Chengsen, et al.
Published: (2025)
by: Wang, Chengsen, et al.
Published: (2025)
The Forecast After the Forecast: A Post-Processing Shift in Time Series
by: Liang, Daojun, et al.
Published: (2026)
by: Liang, Daojun, et al.
Published: (2026)
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
by: Zhang, Xu, et al.
Published: (2026)
by: Zhang, Xu, et al.
Published: (2026)
Efficient Model Selection for Time Series Forecasting via LLMs
by: Wei, Wang, et al.
Published: (2025)
by: Wei, Wang, et al.
Published: (2025)
Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
by: Wang, Yuxuan, et al.
Published: (2025)
by: Wang, Yuxuan, et al.
Published: (2025)
A Lightweight Sparse Interaction Network for Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2026)
by: Zhang, Xu, et al.
Published: (2026)
Deep Coupling Network For Multivariate Time Series Forecasting
by: Yi, Kun, et al.
Published: (2024)
by: Yi, Kun, et al.
Published: (2024)
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
by: Fan, Wei, et al.
Published: (2024)
by: Fan, Wei, et al.
Published: (2024)
Deep Learning for Time Series Forecasting: A Survey
by: Kong, Xiangjie, et al.
Published: (2025)
by: Kong, Xiangjie, et al.
Published: (2025)
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
by: Qi, Shiyi, et al.
Published: (2024)
by: Qi, Shiyi, et al.
Published: (2024)
Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
by: Wang, Jiacheng, et al.
Published: (2026)
by: Wang, Jiacheng, et al.
Published: (2026)
Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting
by: Li, Yuqi, et al.
Published: (2025)
by: Li, Yuqi, et al.
Published: (2025)
Implicit Reasoning in Deep Time Series Forecasting
by: Potosnak, Willa, et al.
Published: (2024)
by: Potosnak, Willa, et al.
Published: (2024)
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
by: Xu, Huibo, et al.
Published: (2025)
by: Xu, Huibo, et al.
Published: (2025)
Empowering Time Series Forecasting with LLM-Agents
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)
ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
by: Wang, Tingting, et al.
Published: (2026)
by: Wang, Tingting, et al.
Published: (2026)
Similar Items
-
ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting
by: Wang, Fei, et al.
Published: (2025) -
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
by: Li, Yujie, et al.
Published: (2025) -
On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting
by: Fu, Yisong, et al.
Published: (2024) -
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models
by: Shao, Zezhi, et al.
Published: (2025) -
Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates
by: Yu, Chengqing, et al.
Published: (2025)