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Liang Xi

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2 papers

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EAAI Journal 2026 Journal Article

Antinoise Adaptive Time–Frequency Fusion for multivariate time series anomaly detection

Sizhe Huang
Liang Xi
Xunhua Huang
Yuan Cheng
Han Liu

Multivariate time series anomaly detection (MTSAD) is critical for ensuring the security of various related Cyber-Physical Systems (CPS), which are highly dynamic and prone to diverse anomaly patterns. While previous studies have demonstrated the effectiveness of frequency-domain features in enhancing detection performance, existing methods face two key limitations: (i) they fail to balance the contributions of time-domain and frequency-domain features, and (ii) they overlook noise-induced cognitive bias, which is common in various CPS scenarios, leading to misjudgment of normal and abnormal patterns. To address these challenges, we propose an AntiNoise Adaptive Time–Frequency Fusion method for MTSAD (ANAF). ANAF dynamically integrates time- and frequency-domain features with an adaptive weighting strategy and employs a residual structure to preserve temporal dependencies. Furthermore, we design an uncertainty-aware detection strategy with fused features as inputs, to perceive noise-induced cognitive biases and enhance detection robustness and accuracy. Experimental results on four benchmark datasets show that ANAF outperforms state-of-the-art methods, specifically, improving the average F1 score by 11. 09%.

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EAAI Journal 2022 Journal Article

Semi-supervised Time Series Classification Model with Self-supervised Learning

Liang Xi
Zichao Yun
Han Liu
Ruidong Wang
Xunhua Huang
Haoyi Fan

Semi-supervised learning is a powerful machine learning method. It can be used for model training when only part of the data are labeled. Unlike discrete data, time series data generally have some temporal relation, which can be considered as a supervised signal in semi-supervised learning to supervise the learning of unlabeled time series data. However, the currently known semi-supervised time series classification (TSC) methods always ignore or under-explore the temporal relation structure and fail to fully use the unlabeled time series data. Therefore, we propose a Semi-supervised Time Series Classification Model with Self-supervised Learning (SSTSC). It takes self-supervised learning as the auxiliary task and jointly optimizes it with the main TSC task. Specifically, it performs the TSC task on the labeled time series data; For the unlabeled time series data, it splits the “past-anchor-future” segments and constructs the positive/negative temporal relation samples with different combinations to accurately predict the temporal relations and capture the higher-quality semantic context in self-supervised learning as a supervised signal for TSC task. Experimental results demonstrate that SSTSC has better effects than the baselines from different perspectives.

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