arXiv:2310.10196v3 Announce Type: replace Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record dynamic system behaviors and enable a wide range of downstream tasks. Effectively analyzing such data is crucial to unlocking their rich information content. Recent advances in large language models and other foundation models have accelerated their use in time series and spatio-temporal data mining. These approaches not only improve pattern recognition and reasoning
The proliferation of real-world temporal and spatio-temporal data, coupled with rapid advancements in large AI models, is driving a critical need for effective analysis techniques.
Sophisticated analysis of dynamic system behaviors, from climate patterns to financial markets, is crucial for decision-making and developing powerful AI applications across various sectors.
The application of large language models and foundation models is significantly improving the ability to extract insights and predict from complex temporal data, potentially unlocking new capabilities.
- · AI/ML researchers
- · Spatio-temporal data analytics platforms
- · Industries with rich time-series data (e.g., finance, logistics)
- · Sensor manufacturers
- · Legacy time series analysis methods
- · Companies unable to leverage large model capabilities
More accurate predictions and anomaly detection in diverse temporal datasets become possible.
New AI-driven services and products emerge that rely on advanced temporal data understanding.
The ability to model and predict complex real-world systems, from climate to urban dynamics, is fundamentally enhanced, driving systemic optimization.
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