---
title: "面向不规则时间序列的因果发现方法"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.18226v1"
permalink: "https://feed.astralor.com/items/itm_047ae2d4472643e1c7919de6"
published: "2026-07-20T17:57:45.000Z"
score: 72
channel: "ai"
tags: ["PCMCI+","不规则时间序列","因果发现","窗口聚合"]
---

# 面向不规则时间序列的因果发现方法

## AI 点评

> 这篇论文对因果发现领域是个有价值的补充，尤其适合从事传感器分析、医疗时序建模或金融交易流建模的读者。扩展PCMCI+的思路简单直观，但实验结果扎实，值得关注后续真实场景应用。

## 速览

该论文提出了一种扩展的PCMCI+方法，用于处理不规则采样时间序列上的因果发现。传统方法依赖固定滞后结构，不适用于传感器流、医疗数据和金融交易等不规则场景。新方法通过预定义时间窗口聚合因果影响，在合成数据上相比标准PCMCI+显著提升了因果图恢复性能。

## 关键点

- 传统因果发现方法依赖规则离散滞后，无法处理不规则时间序列
- 提出PCMCI+的扩展，通过时间窗口聚合因果影响
- 在合成不规则事件流上验证，优于标准PCMCI+
- 适用于传感器、医疗和金融等实际领域

## 全文

Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions. In this work, we propose an extension of PCMCI+, a state-of-the-art method for causal discovery on regular multivariate time series, to allow for handling irregular time series. Instead of modelling causal relations through fixed-lag dependencies, our method aggregates causal influence over predefined temporal windows. We evaluate our method on synthetic irregular event streams with known causal structures under different signal-to-noise ratios, showing that it consistently recovers the underlying causal graph and substantially outperforms the standard PCMCI+ on irregularly sampled data.

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