arXiv:2605.23632v1 Announce Type: new Abstract: We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of normalizing flows for univariate marginals with the consistency and flexibility of a Gaussian Mixture Copula for the joint dependency structure. Our experiments show that copula-based approaches, which decouple the marginals from the joint, yield better marginal models than architectures that directly fit the full joint. With CopFITi, we propose the first IMTS copula that is marginalization-consis
The increasing complexity and irregularity of real-world data streams, particularly in time-series analysis, are driving the need for more robust and expressive probabilistic forecasting models in domains like AI and machine learning.
This development improves the accuracy and reliability of forecasting for irregular multivariate time series, which is critical for applications like financial modeling, sensor data analysis, and predictive maintenance, enhancing decision-making in complex systems.
The introduction of CopFITi provides a more sophisticated method for modeling dependencies in irregular multivariate time series, potentially leading to better predictive performance compared to existing approaches that struggle with these data characteristics.
- · Machine Learning Researchers
- · Financial Services (quant)
- · IoT Data Analytics (predictive maintenance)
- · Healthcare (patient monitoring)
- · Less robust forecasting models
- · Organizations relying on simpler, less accurate IMTS methods
Improved probabilistic forecasting capabilities for irregular multivariate time series using copula-based methods.
Enhanced decision-making and risk management in sectors heavily reliant on predicting complex, non-uniform data flows like finance, logistics, and climate modeling.
Potential for new AI applications in real-time, irregular data environments, leading to more autonomous and intelligent systems capable of handling uncertainty more effectively.
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Read at arXiv cs.LG