QiYao-I: A Manifold-Based Foundation Model Revolutionizes Irregular Multivariate Time Series Forecasting

Summary:QiYao-I is an innovative foundation model for irregular multivariate time series forecasting. It introduces a novel sampling-conditioned temporal manifold attention mechanism and a dynamic variable interaction mechanism with frequency awareness to address the limitations of existing models in handling irregular time intervals and asynchronous cross-variable dependencies. Extensive experiments on real-world benchmarks demonstrate its superior performance and strong generalization capabilities, of

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Core Breakthroughs

QiYao-I is a novel foundation model designed for irregular multivariate time series forecasting. It addresses the limitations of existing models in handling irregular time intervals and asynchronous cross-variable dependencies through the following key innovations:

• Sampling-Conditioned Temporal Manifold Attention Mechanism: By mapping real timestamps into a learnable temporal manifold feature space and injecting temporal manifold biases into attention layers, the model effectively captures irregular time intervals and local sampling structures.
• Dynamic Variable Interaction Mechanism: The model selectively performs cross-variable message passing under asynchronous observations, better handling complex dependencies between multiple variables.

Technical Details

1. Manifold Attention Mechanism:

   • Maps time stamps into a manifold space, enhancing the model's representational capacity by learning temporal manifold features.
   • Injects temporal manifold biases into attention layers to help the model better adapt to irregular time series data.

2. Dynamic Variable Interaction:

   • Employs a frequency-aware approach to dynamically adjust the strength and scope of cross-variable interactions.
   • Adapts interaction paths based on data characteristics under asynchronous observations.

Experimental Results

QiYao-I significantly outperforms existing time series foundation models and end-to-end irregular forecasting models across multiple real-world irregular multivariate time series forecasting benchmarks. Its strong generalization capabilities in zero-shot and few-shot settings demonstrate its potential for complex time series prediction tasks.

Industry Impact and Developer Recommendations

The release of QiYao-I provides a new technical option for the time series forecasting field, particularly excelling in scenarios involving complex and irregular data. For developers, the following points are worth noting:

• Model Applicability: QiYao-I is suitable for applications requiring the processing of irregular time series data, such as finance, healthcare, and industrial IoT.
• Technical Integration: Developers are advised to evaluate QiYao-I's performance in real-world applications by integrating it with existing time series processing tools and platforms.
• Continuous Optimization: As the model is further optimized and expanded, it is expected to achieve breakthroughs in more fields and scenarios.

Conclusion

The introduction of QiYao-I marks a significant advancement in the field of irregular multivariate time series forecasting. Its innovative manifold attention mechanism and dynamic variable interaction mechanism provide new ideas and methods for handling complex time series data.

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Source: ArXiv Machine Learning (cs.LG) (2026-10-07)

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Tags: #QiYao-I Follow #Time Series Forecasting Follow #Manifold Model Follow #Foundation Model Follow #Multivariate Data Follow