arXiv Machine Learning

TA-SparseMG: Trend-Aware Sparse Forecasting via Multi-Scale Gating for Long-Term Time Series

arXiv:2606. 27908v1 Announce Type: new Abstract: Long-term time series forecasting finds extensive applications in domains such as power demand, traffic flow, meteorological observation, and renewable energy dispatch.

arXiv Machine Learning

Jun 2

FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting

arXiv:2606. 01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes.

By Peng He, Yao Liu, Yanglei Gan, Run Lin, Yuxiang Cai, Qiao Liu

llms benchmarks safety

arXiv AI

Jun 26

PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting

arXiv:2606. 26549v1 Announce Type: new Abstract: Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction.

By Ao Hu, Liangjian Wen, Jiang Duan, Yong Dai, He Yan, Dongkai Wang, Jun Wang, Yukun Zhang, Ruoxi Jiang, Zenglin Xu

llms benchmarks

arXiv Machine Learning

Jul 20

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

arXiv:2607. 15705v1 Announce Type: new Abstract: Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems.

By Matthias Hertel, Sebastian P\"utz, Jonathan Kolar, Benjamin Sch\"afer, Ralf Mikut, Veit Hagenmeyer

llms benchmarks

arXiv AI

Aug 7

Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising

arXiv:2510. 05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices.

By Kangjia Yan, Chenxi Liu, Hao Miao, Xinle Wu, Yan Zhao, Chenjuan Guo, Bin Yang

llms efficiency benchmarks

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks safety

arXiv Machine Learning

Jul 21

HyBDM: Multi-Scale Hybrid Experts for Time Series Forecasting with Bidirectional Dependency Modeling

arXiv:2607. 16882v1 Announce Type: new Abstract: Time series forecasting (TSF) is vital to many applications, yet existing models often struggle to capture the heterogeneous long-range global patterns and short-range local variations in multivariate time series.

By Wenqiang Ma, Chen Cheng, Xue Cheng, Jiarui Ye

llms benchmarks

arXiv Machine Learning

Jul 3

Extreme Adaptive Transformer for Time Series Forecasting

arXiv:2607. 02437v1 Announce Type: new Abstract: Time series forecasting remains challenging when the underlying data contain rare but critical extreme events.

By Sanjeev Shrestha, Hui Liu, Yifan Zhang

llms benchmarks

arXiv Machine Learning

Jun 3

TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

arXiv:2606. 03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring.

By Zhe Li, Jindong Tian, Hao Miao, Zhi Lei, Chenjuan Guo, Bin Yang

nlp benchmarks

arXiv Machine Learning

Jul 13

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting

arXiv:2607. 09537v1 Announce Type: new Abstract: Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence.

By Qitai Tan, Ruiwen Gu, Yilin Su, Mo Li, Xu Lin, Xiao-Ping Zhang

benchmarks

arXiv AI

Jul 14

Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series

arXiv:2607. 10740v1 Announce Type: cross Abstract: The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns and suppressing noise appropriately.

By HaoChong Fu, Jian Xu

rag benchmarks

arXiv Machine Learning

Aug 6

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.

By Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme

benchmarks

arXiv AI

Jun 26

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting

arXiv:2601. 16632v4 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning.

By Haonan Yang, Jianchao Tang, Zhuo Li

benchmarks

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