An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2208.09240v1
• Date: Fri, 19 Aug 2022 09:34:11 GMT
• Title: An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection
• Authors: Qiucheng Miao, Chuanfu Xu, Jun Zhan, Dong zhu, Chengkun Wu
• Abstract summary: This paper proposes an anomaly detection method based on unsupervised Short- and Long-term Mask Representation learning (SLMR) Experiments show that the performance of our method outperforms other state-of-the-art models on three real-world datasets.
• Score: 2.387411589813086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection of multivariate time series is meaningful for system behavior monitoring. This paper proposes an anomaly detection method based on unsupervised Short- and Long-term Mask Representation learning (SLMR). The main idea is to extract short-term local dependency patterns and long-term global trend patterns of the multivariate time series by using multi-scale residual dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore, our approach can comprehend temporal contexts and feature correlations by combining spatial-temporal masked self-supervised representation learning and sequence split. It considers the importance of features is different, and we introduce the attention mechanism to adjust the contribution of each feature. Finally, a forecasting-based model and a reconstruction-based model are integrated to focus on single timestamp prediction and latent representation of time series. Experiments show that the performance of our method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method is good at interpretability.

Related papers

• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting [49.1574468325115]
  We present a novel feature selection method embedded in Long Short-Term Memory networks. Our approach optimize the weights and biases of the LSTM in a partitioned manner. Experimental evaluations on air quality time series data from Italy and southeast Spain demonstrate that our method substantially improves the ability generalization of conventional LSTMs.
  arXiv  Detail & Related papers  (2023-12-29T08:42:10Z)
• MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting [19.573651104129443]
  Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intricate patterns of time series by decomposition-based or sampling-based methods. We propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.
  arXiv  Detail & Related papers  (2023-06-12T07:00:37Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• A Pattern Discovery Approach to Multivariate Time Series Forecasting [27.130141538089152]
  State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series.
  arXiv  Detail & Related papers  (2022-12-20T14:54:04Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• Multivariate Time-series Anomaly Detection via Graph Attention Network [27.12694738711663]
  Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. One major limitation is that they do not capture the relationships between different time-series explicitly. We propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue.
  arXiv  Detail & Related papers  (2020-09-04T07:46:19Z)
• Interpretable Deep Representation Learning from Temporal Multi-view Data [4.2179426073904995]
  We propose a generative model based on variational autoencoder and a recurrent neural network to infer the latent dynamics for multi-view temporal data. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.
  arXiv  Detail & Related papers  (2020-05-11T15:59:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.