Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Chen, Hongsong; Li, Xingyu; Liu, Wenmao

doi:10.1016/j.cose.2023.103581

Computers & Security

2024

DOI: 10.1016/j.cose.2023.103581

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Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Hongsong Chen,

Xingyu Li,

Wenmao Liu

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Cited by 2 publications

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Dynamic Memristors for Temporal Signal Processing

Song,

Shao,

Ming

et al. 2024

Adv Materials Technologies

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The rapid advancement of neuromorphic computing demands innovative hardware solutions capable of efficiently mimicking the functionality of biological neural systems. In this context, dynamic memristors have emerged as promising candidates for realizing neuromorphic reservoir computing (RC) architectures. The dynamic memristors characterized by their ability to exhibit nonlinear conductance variations and transient memory behaviors offer unique advantages for constructing RC systems. Unlike recurrent neural networks (RNNs) that face challenges such as vanishing or exploding gradients during training, RC leverages a fixed‐size reservoir layer that acts as a nonlinear dynamic memory. Researchers can capitalize on their adaptable and efficient characteristics by integrating dynamic memristors into RC systems to enable rapid information processing with low learning costs. This perspective provides an overview of the recent developments in dynamic memristors and their applications in neuromorphic RC. It highlights their potential to revolutionize artificial intelligence hardware by offering faster learning speeds and enhanced energy efficiency. Furthermore, it discusses challenges and opportunities associated with integrating dynamic memristors into RC architectures, paving the way for developing next‐generation cognitive computing systems.

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Dynamic Memristors for Temporal Signal Processing

Song,

Shao,

Ming

et al. 2024

Adv Materials Technologies

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Anomaly detection in sensor data via encoding time series into images

Ma,

Wang

2024

Journal of King Saud University - Computer and Information Scie

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Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Cited by 2 publications

References 22 publications

Dynamic Memristors for Temporal Signal Processing

Dynamic Memristors for Temporal Signal Processing

Anomaly detection in sensor data via encoding time series into images

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