Causal Discovery from Temporal Data: An Overview and New Perspectives

• URL: http://arxiv.org/abs/2303.10112v3
• Date: Thu, 3 Aug 2023 16:04:48 GMT
• Title: Causal Discovery from Temporal Data: An Overview and New Perspectives
• Authors: Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li and Jingping Bi
• Abstract summary: Analyzing temporal data is extremely valuable for various applications. causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions.
• Score: 6.251443497694126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

Related papers

• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Causal discovery for time series with constraint-based model and PMIME measure [0.0]
  We present a novel approach for discovering causality in time series data that combines a causal discovery algorithm with an information theoretic-based measure. We evaluate the performance of our approach on several simulated data sets, showing promising results.
  arXiv  Detail & Related papers  (2023-05-31T09:38:50Z)
• A Survey on Causal Discovery Methods for I.I.D. and Time Series Data [4.57769506869942]
  Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data. We present an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (I.I.D.) data and time series data.
  arXiv  Detail & Related papers  (2023-03-27T09:21:41Z)
• Deep Learning for Time Series Anomaly Detection: A Survey [53.83593870825628]
  Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning.
  arXiv  Detail & Related papers  (2022-11-09T22:40:22Z)
• 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)
• Causal Discovery from Sparse Time-Series Data Using Echo State Network [0.0]
  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.
  arXiv  Detail & Related papers  (2022-01-09T05:55:47Z)
• Path Signature Area-Based Causal Discovery in Coupled Time Series [0.0]
  We propose the application of confidence sequences to analyze the significance of the magnitude of the signed area between two variables. This approach provides a new way to define the confidence of a causal link existing between two time series.
  arXiv  Detail & Related papers  (2021-10-23T19:57:22Z)
• 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)
• Causal Inference for Time series Analysis: Problems, Methods and Evaluation [11.925605453634638]
  Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. We focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data.
  arXiv  Detail & Related papers  (2021-02-11T03:26:11Z)
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data [63.15776078733762]
  We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
  arXiv  Detail & Related papers  (2020-06-18T19:59:12Z)

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.