Causal Discovery from Sparse Time-Series Data Using Echo State Network
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 modelling) 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryregressionTime SeriesTime Series AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery …
Causal DiscoveryTime SeriesTime Series AnalysisCausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-wild evaluation of these methods is stil…
Anomaly DetectionBenchmarkingCausal DiscoverySynthetic Data Evaluation+2CUTS: Neural Causal Discovery from Irregular Time-Series Data
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neura…
Causal DiscoveryGraph Neural NetworkIrregular Time SeriesTime Series+1An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear G…
Time SeriesTime Series AnalysisFrom Causal Discovery to Dynamic Causal Inference in Neural Time Series
Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied i…
Causal Inference