Identifying the root causes of anomalies in multivariate time series is challenging due to the complex dependencies among the series. In this paper, we propose a comprehensive approach called AERCA that inherently integrates Granger causal discovery with root cause analysis. By defining anomalies as interventions on the exogenous variables of time series, AERCA not only learns the Granger causality among time series but also explicitly models the distributions of exogenous variables under normal conditions. AERCA then identifies the root causes of anomalies by highlighting exogenous variables that significantly deviate from their normal states. Experiments on multiple synthetic and real-world datasets demonstrate that AERCA can accurately capture the causal relationships among time series and effectively identify the root causes of anomalies.

In this paper, we introduce a novel framework, Neural-HATS for inferring causal structures in time series data using a hybrid method. Neural-HATS uniquely combines conditional independence (CI) testing with continuous optimization-based learning methods to enhance causal discovery. Specifically, it leverages an attention-based encoder-decoder architecture with Kernel Conditional Independence (KCI) testing to enable direct CI tests between time series. These CI test results are integrated into continuous optimization algorithms, enhancing both causal inference accuracy and the effectiveness of continuous optimization models. Experimental evaluations demonstrate that Neural-HATS achieves improved causal graph accuracy.

Causal structure learning from observational data is an active field of research over the past decades. Although many approaches exist, such as constrained-based methods and score-based methods including the emerging deep learning-based methods, most of them address the static, non-dynamic setting. In this paper, we propose a score-based causal discovery algorithm named Neural Time-invariant Causal Discovery (NTiCD), which learns summary causal graphs from multivariate time series data based on the principle of Granger causality. NTiCD is a continuous optimization-based technique that leverages the power of deep neural networks to compute the score values. To this end, we use an LSTM to obtain the hidden non-linear representations of temporal variables in the time series data. Then, these features are aggregated using graph convolutional networks and decoded using an MLP that outputs the forecast of the future data values in the time series. The model is optimized based on a score function subject to regularized loss. The final output is a summary causal graph that captures the time-invariant causal relations within and between time series. We evaluate the performance of our algorithm on several synthetic and real datasets. The result analysis over a number of different datasets demonstrates the improvement in the accuracy of causal structure discovery of temporal data compared to other state-of-the-art methods.

Discovering causal structure from temporal data is an important problem in many fields in science. Existing methods usually suffer from several limitations such as assuming linear dependencies among features, limiting to discrete time series, and/or assuming stationarity, i.e., causal dependencies are repeated with the same time lag and strength at all time points. In this paper, we propose an algorithm called the μ-PC that addresses these limitations. It is based on the theory of μ-separation and extends the well-known PC algorithm to the time domain. To be applicable to both discrete and continuous time series, we develop a conditional independence testing technique for time series by leveraging the Recurrent Marked Temporal Point Process (RMTPP) model. Experiments using both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.