Publications

2026

1. FIN

   Pattern-Guided Forecasting Framework for Metal Price Prediction with Grouping Decomposed Series

   Dongbin Kim, Jaewook Lee, and Hoki Kim

   Financial Innovation, 2026

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   Accurate forecasting of precious metal prices is increasingly critical in modern financial markets, as these metals function as industrial commodities and as strategic financial instruments for portfolio diversification and risk management. Although recent advances in financial technology have produced a range of forecasting approaches from traditional econometric methods to sophisticated deep learning models—the complex dynamics of metal prices continue to challenge existing methodologies. This paper introduces a significant innovation in financial forecasting by revealing and leveraging previously unrecognized pattern relationships in decomposed time series data. Our comprehensive analysis of metal price dynamics reveals distinct grouped patterns in decomposed time series components, challenging the conventional assumption of independence in current forecasting methods. Based on these insights, we propose the pattern-guided forecasting framework (PGFF), which enhances forecasting accuracy by leveraging cross-dimensional pattern relationships in decomposed time series. Our framework employs a novel two-stage approach: first, categorizing decomposed time series based on their temporal characteristics and autocorrelation patterns; then, implementing cross-dimensional forecasting to capture complex market dynamics. Empirical analysis of four major precious metals demonstrates that PGFF consistently outperforms existing forecasting frameworks, offering significant implications for investment decision-making and portfolio management in modern financial markets.

   @article{kim2026pgff,
     title = {Pattern-Guided Forecasting Framework for Metal Price Prediction with Grouping Decomposed Series},
     author = {Kim, Dongbin and Lee, Jaewook and Kim, Hoki},
     journal = {Financial Innovation},
     volume = {12},
     number = {1},
     pages = {45},
     year = {2026},
     doi = {10.1186/s40854-025-00833-5},
   }

2. ICLR

   Local Geometry Attention for Time Series Forecasting under Realistic Corruptions

   Dongbin Kim, Youngjoo Park, Woojin Jeong, and 1 more author

   In The Fourteenth International Conference on Learning Representations, 2026

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   Transformers have demonstrated strong performance in time series forecasting, yet they often fail to capture the intrinsic structure of temporal data, making them susceptible to real-world noise and anomalies. Unlike in vision or language, the local geometry of temporal patterns is a critical feature in time series forecasting, but it is frequently disrupted by corruptions. In this work, we address this gap with two key contributions. First, we propose Local Geometry Attention (LGA), a novel attention mechanism theoretically grounded in local Gaussian process theory. LGA adapts to the intrinsic data geometry by learning query-specific distance metrics, enabling it to model complex temporal dependencies and enhance resilience to noise. Second, we introduce TSRBench, the first comprehensive benchmark for evaluating forecasting robustness under realistic, statistically-grounded corruptions. Experiments on TSRBench show that LGA significantly reduces performance degradation, consistently outperforming both Transformer and linear model. These results establish a foundation for developing robust time series models that can be deployed in real-world applications where data quality is not guaranteed. Our code is available at: https://github.com/dongbeank/LGA.

   @inproceedings{kim2026lga,
     title = {Local Geometry Attention for Time Series Forecasting under Realistic Corruptions},
     author = {Kim, Dongbin and Park, Youngjoo and Jeong, Woojin and Lee, Jaewook},
     booktitle = {The Fourteenth International Conference on Learning Representations},
     year = {2026},
   }

2025

1. EAAI

   Temporal consistency ensemble empirical mode decomposition for forecasting practical metal price

   Yujin Choi, Dongbin Kim, and Jaewook Lee

   Engineering Applications of Artificial Intelligence, 2025

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   Accurately forecasting metal prices is critical to economic, industrial, and energy markets. However, traditional time series models often rely on future data, limiting their real-world applicability. This study found that decomposition methods utilizing future data inflate model performance, and the high accuracy of forecasting models is mainly due to these unrealistic assumptions. In this paper, we propose a novel Temporal Consistency Ensemble Empirical Mode Decomposition (TC-EEMD) method d...

   @article{choi2025temporal,
     title = {Temporal consistency ensemble empirical mode decomposition for forecasting practical metal price},
     author = {Choi, Yujin and Kim, Dongbin and Lee, Jaewook},
     journal = {Engineering Applications of Artificial Intelligence},
     volume = {158},
     pages = {111490},
     year = {2025},
     doi = {10.1016/j.engappai.2025.111490},
   }

2024

1. NeurIPS

   Are Self-Attentions Effective for Time Series Forecasting?

   Dongbin Kim, Jinseong Park, Jaewook Lee, and 1 more author

   Advances in Neural Information Processing Systems, 2024

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   50+ citations

   Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformers have dramatically advanced the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift the focus from evaluating the overall Transformer architecture to specifically examining the effectiveness of self-attention for time series forecasting. To this end, we introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional transformer framework by eliminating self-attention and leveraging cross-attention mechanisms instead. By establishing future horizon-dependent parameters as queries and enhanced parameter sharing, our model not only improves long-term forecasting accuracy but also reduces the number of parameters and memory usage. Extensive experiment across various datasets demonstrates that our model achieves superior performance with the lowest mean squared error and uses fewer parameters compared to existing models. The implementation of our model is available at: https://github.com/dongbeank/CATS.

   @article{kim2024self,
     title = {Are Self-Attentions Effective for Time Series Forecasting?},
     author = {Kim, Dongbin and Park, Jinseong and Lee, Jaewook and Kim, Hoki},
     journal = {Advances in Neural Information Processing Systems},
     volume = {37},
     pages = {114180--114209},
     year = {2024},
   }