A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
数据驱动的两阶段多划分时间序列因果集成模型AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以时间序列为数据对象，核心任务是通过多算法集成检测因果关系。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
数据驱动的两阶段多划分时间序列因果集成模型
- 研究问题
- 时间序列因果检测算法对非线性、噪声和耦合结构的适应性不同，如何组合多个算法，减少同一数据上因果关系判断的不一致？
- 方法与思路
- 作者先多次划分数据，在各部分运行基础算法，并用高斯混合模型筛选可能有效的关系；第二阶段再按三种规则合并各算法结果。
- 材料陈述的贡献
- 摘要提出两阶段因果集成方法和新的评价指标，在三个具有已知因果真值的合成数据集上报告集成方法优于其各个基础算法。
- 阅读关注点
- 应核对划分方式是否保留时间依赖、高斯混合筛选及合并规则的依据，并检查新评价指标和合成数据是否偏向特定算法。
- 阅读边界
- 这是 arXiv 预印本，未核实同行评审或期刊接收。摘要未说明因果识别假设或实际数据验证，合成实验优势不能保证真实场景可靠性。
原文摘要
Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. Therefore, different algorithms often generate different causal relationships for the same input. To achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in the first phase. To achieve this, the data are initially divided into several partitions and the base algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which are specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results.