A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series
基于特征与信息论识别可解释的长时间尺度序列交互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：摘要直接研究时间序列的滑窗特征及序列间依赖检测，对应时间序列方法；所列金融等领域仅是潜在应用。
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…
基于特征与信息论识别可解释的长时间尺度序列交互
- 研究问题
- 当序列间联系由较长时间窗口中的统计特征传递时，如何从短而嘈杂的数据中检测依赖并解释交互形式？
- 方法与思路
- 作者从来源序列的滑动窗口提取候选特征，以信息论方法评估其与目标过程数值的依赖，并比较三类生成过程模拟。
- 材料陈述的贡献
- 摘要称，特征方法在短序列、高噪声和长交互尺度情景中可优于直接使用原始数值的方法，并提供交互解释信息。
- 阅读关注点
- 核对信息量估计、特征筛选、窗口与时间滞后选择，以及比较方法和有限样本下多重搜索的处理方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明具体统计条件，检测到依赖不能直接证明因果机制。
原文摘要
Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for detecting pairwise dependence of time series, such as Pearson correlation, Granger causality, and mutual information, are computed directly in the space of measured time-series values. But for systems in which interactions are mediated by statistical properties of the time series (`time-series features') over longer timescales, this approach can fail to capture the underlying dependence from limited and noisy time-series data, and can be challenging to interpret. Addressing these issues, here we introduce an information-theoretic method for detecting dependence between time series mediated by time-series features that provides interpretable insights into the nature of the interactions. Our method extracts a candidate set of time-series features from sliding windows of the source time series and assesses their role in mediating a relationship to values of the target process. Across simulations of three different generative processes, we demonstrate that our feature-based approach can outperform a traditional inference approach based on raw time-series values, especially in challenging scenarios characterized by short time-series lengths, high noise levels, and long interaction timescales. Our work introduces a new tool for inferring and interpreting feature-mediated interactions from time-series data, contributing to the broader landscape of quantitative analysis in complex systems research, with potential applications in various domains including but not limited to neuroscience, finance, climate science, and engineering.