TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
TS-CausalNN：从非线性非平稳时间序列学习时序因果关系AI 解读 ↓
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TS-CausalNN：从非线性非平稳时间序列学习时序因果关系
- 研究问题
- 面对非线性、非平稳且含噪的时间序列，如何同时学习同期与滞后的因果关系，并控制结构学习的计算成本？
- 方法与思路
- 作者提出含并行定制因果层的卷积网络，加入无环约束并采用增广拉格朗日优化，在合成及真实数据上比较方法表现。
- 材料陈述的贡献
- 摘要称该模型能够处理非平稳性与非线性，实验表现具有竞争力，真实数据推断图与领域理解较为一致。
- 阅读关注点
- 应核对时间序列因果识别假设、非平稳性的具体处理、同期无环约束，以及图恢复指标、噪声设定和计算成本。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未列出识别条件或实验数值，与领域理解一致不等于因果验证。
原文摘要
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data. However, the majority of current time series causal discovery methods assume stationarity and linear relations in data, making them infeasible for the task. Further, the recent deep learning-based methods rely on the traditional causal structure learning approaches making them computationally expensive. In this paper, we propose a Time-Series Causal Neural Network (TS-CausalNN) - a deep learning technique to discover contemporaneous and lagged causal relations simultaneously. Our proposed architecture comprises (i) convolutional blocks comprising parallel custom causal layers, (ii) acyclicity constraint, and (iii) optimization techniques using the augmented Lagrangian approach. In addition to the simple parallel design, an advantage of the proposed model is that it naturally handles the non-stationarity and non-linearity of the data. Through experiments on multiple synthetic and real world datasets, we demonstrate the empirical proficiency of our proposed approach as compared to several state-of-the-art methods. The inferred graphs for the real world dataset are in good agreement with the domain understanding.