Causal Discovery from Sparse Time-Series Data Using Echo State Network

• URL: http://arxiv.org/abs/2201.02933v1
• Date: Sun, 9 Jan 2022 05:55:47 GMT
• Title: Causal Discovery from Sparse Time-Series Data Using Echo State Network
• Authors: Haonan Chen (1), Bo Yuan Chang (1), Mohamed A. Naiel1 (1), Georges Younes (1), Steven Wardell (2), Stan Kleinikkink (2), John S. Zelek (1) ((1) University of Waterloo, (2) ATS Automation)
• Abstract summary: Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. We propose a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network. We report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. However, reliable causal discovery can be very challenging, especially when the data acquisition rate varies (i.e., non-uniform data sampling), or in the presence of missing data points (e.g., sparse data sampling). To address these issues, we proposed a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network, which is a type of reservoir computer (i.e., used for chaotic system modeling) for Causal discovery. We evaluate the performance of our proposed system against three other off-the-shelf causal discovery algorithms, namely, structural expectation-maximization, sub-sampled linear auto-regression absolute coefficients, and multivariate Granger Causality with vector auto-regressive using the Tennessee Eastman chemical dataset; we report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms, demonstrating the viability of our approach to discover causal relationships in a complex system with missing entries.

Related papers

• DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems [3.44012349879073]
  We present DeepHYDRA (Deep Hybrid DBSCAN/Reduction-Based Anomaly Detection) It combines DBSCAN and learning-based anomaly detection. It is shown to reliably detect different types of anomalies in both large and complex datasets.
  arXiv  Detail & Related papers  (2024-05-13T13:47:15Z)
• 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)
• MIM-GAN-based Anomaly Detection for Multivariate Time Series Data [7.734588574138427]
  The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. We propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN) Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2023-10-26T02:09:39Z)
• CUTS+: High-dimensional Causal Discovery from Irregular Time-series [13.84185941100574]
  We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS. We show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.
  arXiv  Detail & Related papers  (2023-05-10T04:20:36Z)
• CUTS: Neural Causal Discovery from Irregular Time-Series Data [27.06531262632836]
  Causal discovery from time-series data has been a central task in machine learning. We present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.
  arXiv  Detail & Related papers  (2023-02-15T04:16:34Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

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.