11 papers
REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
Xu Zhang, Chang Xu, Hui Sun +5
Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this by combining complementary m…
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Xu Zhang, Junwei Deng, Chang Xu +2
Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. These assumptions are often violated in…
Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
Zhao Tan, Yiji Zhao, Shiyu Wang +5
Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records. H…
EventTSF: Event-Aware Non-Stationary Time Series Forecasting
Yunfeng Ge, Ming Jin, Yiji Zhao +4
Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities…
Rethinking Data Mixing from the Perspective of Large Language Models
Yuanjian Xu, Tianze Sun, Changwei Xu +7
Data mixing strategy is essential for large language model (LLM) training. Empirical evidence shows that inappropriate strategies can significantly reduce generalization. Although…
OATS: Online Data Augmentation for Time Series Foundation Models
Junwei Deng, Chang Xu, Jiaqi W. Ma +5
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. E…