Long memory network time series
长记忆网络时间序列AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究长记忆多变量时间序列，并将网络边结构用于刻画变量间联系。
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长记忆网络时间序列
- 研究问题
- 当多变量序列具有缓慢衰减的自相关且伴随网络结构时，如何避免传统长记忆模型参数过多和计算困难？
- 方法与思路
- 作者提出两个纳入网络结构的长记忆模型，利用较低维的参数空间，将基于似然的估计算法调整到网络情境。
- 材料陈述的贡献
- 摘要称，模拟显示新估计方法更稳定，能够处理传统模型计算受阻的情境，并以环境及金融数据展示应用。
- 阅读关注点
- 应核对两个模型的长记忆机制、网络已知或估计时的处理、似然计算，以及稳定性比较的维度和基准。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未给出模型方程、适用条件及计算改进的具体幅度。
原文摘要
Many scientific areas, from computer science to the environmental sciences and finance, give rise to multivariate time series which exhibit long memory, or loosely put, a slow decay in their autocorrelation structure. Efficient modelling and estimation in such settings is key for a number of analysis tasks, such as accurate prediction. However, traditional approaches for modelling such data, for example long memory vector autoregressive processes, are challenging even in modest dimensions, as the number of parameters grows quadratically with the number of modelled variables. Additionally, in many practical data settings, the observed series is accompanied by a (possibly inferred) network that provides information about the presence or absence of between-component associations via the graph edge topology. This article proposes two new models for capturing the dynamics of long memory time series where a network is accounted for. Our approach not only facilitates the analysis of graph-structured long memory time series, but also improves computational efficiency over traditional multivariate long memory models by leveraging the inherent low-dimensional parameter space by adapting likelihood-based estimation algorithms to the network setting. Simulation studies show that our proposed estimation is more stable than traditional models, and is able to tackle data scenarios where current models fail due to computational challenges. While widely applicable, here we demonstrate the efficacy of our proposed models on datasets arising in environmental science and finance.