Shylock: Causal Discovery in Multivariate Time Series based on Hybrid Constraints

TL;DR

Shylock is a novel causal discovery method for multivariate time series that effectively works with limited data by reducing parameters and combining global and local constraints, outperforming existing methods.

Contribution

The paper introduces Shylock, a new approach that improves causal discovery in multivariate time series with few data points using hybrid constraints and parameter-efficient modeling.

Findings

Shylock outperforms existing methods on benchmark datasets.

It effectively handles few-shot and normal multivariate time series.

The method reduces model parameters exponentially.

Abstract

Causal relationship discovery has been drawing increasing attention due to its prevalent application. Existing methods rely on human experience, statistical methods, or graphical criteria methods which are error-prone, stuck at the idealized assumption, and rely on a huge amount of data. And there is also a serious data gap in accessing Multivariate time series(MTS) in many areas, adding difficulty in finding their causal relationship. Existing methods are easy to be over-fitting on them. To fill the gap we mentioned above, in this paper, we propose Shylock, a novel method that can work well in both few-shot and normal MTS to find the causal relationship. Shylock can reduce the number of parameters exponentially by using group dilated convolution and a sharing kernel, but still learn a better representation of variables with time delay. By combing the global constraint and the local constraint, Shylock achieves information sharing among networks to help improve the accuracy. To evaluate the performance of Shylock, we also design a data generation method to generate MTS with time delay. We evaluate it on commonly used benchmarks and generated datasets. Extensive experiments show that Shylock outperforms two existing state-of-art methods on both few-shot and normal MTS. We also developed Tcausal, a library for easy use and deployed it on the EarthDataMiner platform