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Jianyong Chen

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4 papers

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AAMAS Conference 2026 Conference Paper

DEMamba: Decoupled Enhanced State Space Models with Selective Mechanisms for Multivariate Time Series Forecasting

Junluo Zheng
Yang Liu
Jianyong Chen

Multivariate time series forecasting (MTSF) is an important and forefront task in many real-world applications. Recently, Mamba has emerged as a powerful alternative to Transformer-based models in sequential modeling, leveraging its selective mechanism to achieve high accuracy and linear complexity. However, existing efforts to apply Mamba to MTSF face limitations, particularly in effectively and efficiently capturing temporal and cross-variate dependencies. In this work, we propose DEMamba, an enhanced model tailored for MTSF. Specifically, we redefine the roles of 1D convolution and selective SSMs (S6) and design the Time-Variable Decoupled Scanning (TVDS) Mamba block. It decouples the learning of different dimensions within a single Mamba block, with 1D convolution capturing temporal dependencies and S6 capturing cross-variate dependencies. Furthermore, we capture intrapatch feature dependencies with a feed-forward network. These designs significantly enhance the ability of DEMamba to handle complex dependencies. Extensive experimental results on eight real-world datasets demonstrate the effectiveness of DEMamba against previous state-of-the-arts. Code is available at this repository: https: //github. com/LiuYang826/DEMamba.

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AAAI Conference 2026 Conference Paper

FeTS: A Feature-Aware Framework for Time Series Forecasting

Le Wang
Jianyong Chen
Songbai Liu

Time series forecasting faces a fundamental challenge: the uneven distribution of predictive importance in time series data, where some specific time points and feature combinations carry disproportionately predictive power. As a result, uniform processing methods that treat all data alike inevitably fall short of optimal performance. To address this problem, we propose FeTS, a feature-aware framework that comprehensively learns temporal features through two key components: (i) Adaptive Feature Extraction (AdaFE), which dynamically discovers the most important features within each temporal patch and extracts them on the fly, yielding sharper and more focused local representations; and (ii) Dual-Scale Feed-Forward Network (DSFFN), which strategically integrates fine-grained local features with global long-term dependencies to achieve richer dual-scale representation learning. Extensive experiments on eight benchmark datasets demonstrate that FeTS achieves state-of-the-art performance in time series forecasting tasks, offering a novel solution to the challenge of uneven predictive importance in forecasting.

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EAAI Journal 2024 Journal Article

A Kriging-assisted evolutionary algorithm with multiple infill sampling for expensive many-objective optimization

Qingling Zhu
Gaoli Kang
Xunfeng Wu
Qiuzhen Lin
Huimei Tang
Jianyong Chen

Surrogate-assisted evolutionary algorithms (SAEAs) have been extensively used to solve computationally expensive multi-objective optimization problems (MOPs) as they can obtain a set of satisfyingly optimal solutions while remaining within a limited computational budget. Nevertheless, the expensive MOPs with more than three objectives have received little attention, and most existing SAEAs fail to achieve satisfactory results when solving them. Therefore, to fill this research gap, a Kriging-assisted evolutionary algorithm with multiple infill sampling for solving expensive many-objective optimization problems is proposed. In this paper, to balance exploration and exploitation, a new environmental selection operator is proposed, which is composed of three procedures conducted consecutively. In addition, a new multiple infill sampling strategy is proposed to select the most representative solutions for real function evaluations and model updates. Furthermore, to limit the computational costs of constructing/updating the surrogate model, a new archive update strategy is proposed to maneuver the training data set. In experiments, our method is verified on some benchmark problems. The experimental results demonstrate that the proposed algorithm shows promising performance when compared with four state-of-the-art SAEAs for solving expensive many-objective optimization problems.

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IJCAI Conference 2024 Conference Paper

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

Yingnan Yang
Qingling Zhu
Jianyong Chen

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https: //github. com/CSyyn/VCformer.

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