Bidirectional Causal Graph Learning through WeightSharing and LowRank Neural Network Discovering the causal graph in multivariate time series data is of great importance for industrial society, yet challenging due to the unknown nonlinearity in the data. Existing works only explore the data in chronological order, and rely on preassumed kernels or certain distribution assumption. In this paper, we present a Bidirectional neural network for Causal Graph Learning (BiCGL) through weightsharing and lowrank neural network. It discovers the causal graph by simultaneously exploring input in forward and reverse chronological order. Both directions approach the same causal graph with shared lowrank approximation, which provides robustness and better accuracy against data noise. Experiments on synthetic and real world datasets prove our BiCGLu0027s outperformance over existing baselines.

VariableLag Granger Causality for Time Series Analysis Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variablelag Granger causality, a generalization of Granger causality that relaxes the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring variablelag Granger causality relations. We demonstrate our approach on an application for studying coordinated collective behavior and show that it performs better than several existing methods in both simulated and realworld datasets. Our approach can be applied in any domain of time series analysis.