3 research outputs found
Variable-lag 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 variable-lag 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 variable-lag 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 real-world datasets. Our approach can be applied in any domain of time
series analysis.Comment: This paper will be appeared in the proceeding of 2019 IEEE
International Conference on Data Science and Advanced Analytics (DSAA). The R
package is available at https://github.com/DarkEyes/VLTimeSeriesCausalit
Estimating the Causal Effect from Partially Observed Time Series
Many real-world systems involve interacting time series. The ability to detect causal dependencies between system components from observed time series of their outputs is essential for understanding system behavior. The quantification of causal influences between time series is based on the definition of some causality measure. Partial Canonical Correlation Analysis (Partial CCA) and its extensions are examples of methods used for robustly estimating the causal relationships between two multidimensional time series even when the time series are short. These methods assume that the input data are complete and have no missing values. However, real-world data often contain missing values. It is therefore crucial to estimate the causality measure robustly even when the input time series is incomplete. Treating this problem as a semi-supervised learning problem, we propose a novel semi-supervised extension of probabilistic Partial CCA called semi-Bayesian Partial CCA. Our method exploits the information in samples with missing values to prevent the overfitting of parameter estimation even when there are few complete samples. Experiments based on synthesized and real data demonstrate the ability of the proposed method to estimate causal relationships more correctly than existing methods when the data contain missing values, the dimensionality is large, and the number of samples is small