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AI Research
arXiv CS.AI·Jun 9·5 min read
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
TL;DR
Temporal data, such as time series and spatio-temporal data, is challenging to analyze due to its complexity and volume. Recent advancements in large language models, like transformers, have been applied to enhance the mining of this data.
✦ Why It Matters
Engineers can leverage large language models to improve the analysis and forecasting of complex temporal data.
Key Takeaways
- Utilize large models for improved time series analysis in your applications.
- Explore domain-specific models for tailored insights in your field.
- Access curated datasets and tools to support your research.
- Stay informed about open research opportunities in temporal data analysis.
Full Summary
Temporal data, which includes time series and spatio-temporal data, is generated in large volumes by sensors and is crucial for understanding dynamic systems. Recent advancements in large models, particularly in machine learning, have enhanced the analysis of this data, improving pattern recognition and reasoning.
This survey categorizes existing models into two groups: large models for time series analysis (LM4TS) and those for spatio-temporal data mining (LM4STD). It further distinguishes between general-purpose and domain-specific models, providing a structured overview of their applications across various fields.
Additionally, the authors curate resources such as datasets and tools to support researchers and practitioners. The survey emphasizes the importance of these models in advancing artificial general intelligence and identifies open research opportunities in temporal data analysis.