The unsupervised anomaly detection in KPI (Key Performance Indicator) series has been an active research area due to its enormous potential for application in industry. KPI series representation, reconstruction, and forecasting have made extraordinary progress in existing work. However, long-term temporal patterns prohibit the model from learning reliable dependencies. To this end, we propose ...

Sep 28, 2022 · To this end, we propose a novel approach based on VAE-TCN hybrid model. Our model utilizes both a VAE (Variational Auto-Encoder) module for forming robust local features over short windows and a TCN (Temporal convolutional network) module for estimating the long term correlation in the series on top of the features inferred from the VAE module.

Sep 28, 2022 · Our model uses VAE (variational automatic coder) to learn robust local features in a short window, and uses TCN (temporal convolution network) to estimate the long-term correlation in the sequence based on the features inferred by VAE module.

Oct 26, 2023 · In the proposed temporal convolutional variational autoencoder—generative adversarial networks (TCVAE-GAN) model, the temporal convolutional network (TCN) module is incorporated into the encoder, decoder, and discriminator to improve the reconstruction and feature extraction capabilities of time-series data.

Jan 1, 2021 · At the same time, a neural network prediction model combining Variational Auto-Encoder (VAE) and Time Convolutional Network (TCN) is proposed to improve the accuracy of equipment health prediction.

Jul 1, 2024 · To test the effectiveness of the VSAD proposed in this paper, we experimentally compare seven state-of-the-art anomaly detection algorithms in the field of MTS anomaly detection, which include TCN-based, graph neural network-based and VAE-based, etc.

Then we used a CNN-VAE model, an improved reconstruction-based anomaly detection method, to compute the reconstruction error in an unsupervised way. The method can be integrated to an end-to-end framework as it is lightweight.

Feb 5, 2024 · Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.

The results show that the proposed TCN-AE-based outlier detection algorithm achieves better detection accuracy in terms of F-score than other DML-based algorithms such as long short-term memory (LSTM)-AE-based algorithm and the convolutional …

We propose a novel unsupervised model for micro-gesture classification, called MSTCN-VAE, which follows the VAE structure by adding the Multi-scale TCN and hidden feature extraction block to the encoder, and the decoder is embedded with the Temporal Deconvolution block.