Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection

Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the effectiveness of popular deep learning methods for TSAD, suggesting their failure in detecting subtle and prolonged anomalies. Outlier Exposure (OE) and Masked Autoencoder (MAE) emerge as two promising paradigms (classification and reconstruction) for solving the above problems. However, OE-based methods are constrained by poor generalization, while MAE-based methods are limited by masking misalignment issues. To address these limitations, this paper proposes a novel framework, CoAD, which unifies the two paradigms to leverage their complementary strengths while mitigating their respective weaknesses. In this framework, the classification module generates probability-informed soft masks for the reconstruction module, which in turn alleviates the generalization problem of the classification module. This cooperative design enables CoAD to effectively detect subtle and complex anomalies that are often overlooked by existing methods. Additionally, the classification module is carefully designed to resolve issues related to improper classification granularity and the neglect of frequency information. Extensive experiments on high-quality benchmark datasets, conducted under rigorous evaluation protocols, demonstrate that CoAD significantly outperforms both state-of-the-art deep learning and traditional data mining methods, highlighting the potential of deep learning in TSAD. Moreover, CoAD is lightweight and substantially faster than existing SOTA methods, demonstrating its practical value for large-scale, real-time applications.

AMPSO: Artificial Multi-Swarm Particle Swarm Optimization

In this paper we propose a novel artificial multi-swarm PSO which consists of an exploration swarm, an artificial exploitation swarm and an artificial convergence swarm. The exploration swarm is set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated. The exploration and exploitation operations are alternatively conducted until the evolution rate of the exploitation is smaller than a threshold or the maximum number of iterations is reached. An adaptive inertia weight strategy is applied to different swarms to guarantee their performances of exploration and exploitaiton. To guarantee the accuracy of the results, a novel diversity scheme based on the positions and fitness values of the particles is proposed to control the exploration, exploitation and convergence processes of the swarms. To mitigate the inefficiency issue due to the use of diversity, two swarm update techniques are proposed to get rid of lousy particles such that nice results can be achieved within a fixed number of iterations. The effectiveness of AMPSO is validated by a set of comparisons with two recent PSO variants and their corresponding sets of comparison algorithms on the CEC2015 test suite.