Shylock通过混合约束实现少样本多变量时间序列因果发现
Shylock: Causal Discovery in Multivariate Time Series based on Hybrid Constraints
- 结合全局与局部约束，用分组扩张卷积共享参数
- 在少样本和常规数据上均超越现有最优方法
- 适合小数据场景下的时序因果分析，易部署于平台
因果关系发现因广泛应用而日益受到关注。现有方法依赖人工经验、统计方法或图模型准则，存在易出错、假设理想化及需大量数据等问题。许多领域存在多变量时间序列（MTS）数据稀缺，导致因果推断困难且易过拟合。为此，本文提出Shylock，一种可在少样本与常规MTS下有效发现因果关系的新方法。通过分组扩张卷积与共享内核，模型参数量指数级减少，同时保留时间延迟的变量表征能力。结合全局与局部约束，实现网络间信息共享，提升准确性。为评估性能，设计生成含时间延迟的MTS数据的方法，在常用基准与生成数据集上进行实验。结果表明，Shylock在少样本与常规场景下均优于两种现有最先进方法。此外，开发了Tcausal工具库，已部署于EarthDataMiner平台。
原文摘要 · Abstract (English)
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
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