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Author: search.filters.author.Chalongvorachai, ThasornSubject: search.filters.subject.Latent Space

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    LSCR: Latent Space Coordination Relation for Anomaly Prediction
    (2023-01-01)
    Chalongvorachai, Thasorn
    ;
    Woraratpanya, Kuntpong
    Anomaly time series prediction is a crucial yet challenging task in real-world systems. Existing techniques often require a substantial amount of data to achieve satisfactory performance, posing a significant challenge as anomaly data is scarce and difficult to obtain. Despite attempts to address this issue using traditional machine learning techniques, their effectiveness remains limited, resulting in performance degradation or costly trade-offs. Therefore, in this paper, we propose a novel approach called Latent Space Coordination Relation for Anomaly Prediction to overcome these challenges. Our framework leverages the power of the Variational Autoencoder (VAE) and learns the coordination relations of points in the latent space to detect anomalies. By exploiting the latent space, our method enables effective learning and prediction of anomalies. Additionally, the decoder of the VAE aids in restoring the data, further improving the accuracy of anomaly detection. Experimental results demonstrate that our approach outperforms baseline models when training data is limited. The predicted anomalous signals exhibit lower error rates, highlighting the efficacy of our method. This improvement is attributed to the utilization of the latent space for learning and assisting in anomaly prediction, along with the signal restoration capabilities of the decoder.
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    3DVAE-ERSG: 3D Variational Autoencoder for Extremely Rare Signal Generation
    (2021-01-01)
    Chalongvorachai, Thasorn
    ;
    Woraratpanya, Kuntpong
    Data generation is not data augmentation. Our data generation is a new technique that can synthesize a dataset from a very small number of samples and ensure the quality of its outputs. Recently, this concept has been proposed and applied in a framework called Data Generation Framework for Extremely Rare Case Signals (DGERS) to solve a problem of a limited number of anomaly signals. With the power of DGERS consisting of principal components, including various data augmentation techniques on diverse domains, Signal Fragment Assembler (SFA), Variational Autoencoder (VAE), Data Picker (DP), and Quality Classifier (QC), the generated dataset had the good quality, when evaluated with a performance tester. Nevertheless, the DGERS has not used the full potential of VAE yet. The previous framework used the VAE latent space in only two dimensions. To use a higher potential of VAE, this paper proposed a 3D Variational Autoencoder for Extremely Rare Signal Generation (3DVAE-ERSG). This method increases the dimension of the latent space from 2D to 3D. We also proposed the 3D Data Picker for data exploration. To test this hypothesis, we experimented with the same datasets that the DGERS method was tested before. The results show that our 3DVAE-ERSG can outperform the baseline in most cases.