arXiv:2606.08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models. However, prior studies indicate that pre-trained LTSMs may exhibit a poorly conditioned non-convex loss landscape, leading to limited trainability. As a result, direct fine-tuning tends to cause overfitting and suboptimal performance, sometimes even worse than training from scratch, substantially diminishing
This research addresses a critical challenge in the development and application of Large Time Series Models (LTSMs), which are gaining prominence similar to Large Language Models in AI capabilities.
Improving the fine-tuning efficiency and performance of LTSMs is crucial for their widespread adoption and impact across various industries dependent on time series data analysis.
The focus on overcoming fine-tuning limitations means LTSMs will become more practical and robust for real-world applications, potentially accelerating their deployment in complex data environments.
- · AI researchers
- · Data scientists
- · Industries relying on time series data
- · Developers of large time series models
- · Tasks currently reliant on inefficient or suboptimal time series analysis method
More effective fine-tuning methods for LTSMs will lead to improved prediction accuracy and model stability.
This advancement could accelerate the development of autonomous systems and financial forecasting models leveraging robust LTSMs.
Enhanced LTSM performance may lead to new breakthroughs in areas like climate modeling, personalized medicine, and resource management through better predictive analytics.
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Read at arXiv cs.LG