Online Sequential Leveraging Sampling Method for Streaming Autoregressive Time Series with Application to Seismic Data
流式自回归时间序列的在线序贯杠杆抽样及地震数据应用AI 解读 ↓
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AI 根据所提供的论文材料归类：分类依据是流式自回归时间序列及其序贯抽样推断方法；地震应用本身不构成宏观经济数据的证据。
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流式自回归时间序列的在线序贯杠杆抽样及地震数据应用
- 研究问题
- 面对高速、海量且存在时间相关性的流式数据，如何在线选取有限样本进行自回归推断，并平衡计算成本与估计准确性？
- 方法与思路
- 作者提出 SLS 方法，依据在线杠杆分数随机选择连续数据块的起点，以序贯停止规则决定块长，并对该块进行最小二乘估计。
- 材料陈述的贡献
- 摘要称，在线抽样后的标准化最小二乘估计在两类自回归设置中渐近正态，并通过地震数据和模拟展示方法表现。
- 阅读关注点
- 应核对杠杆分数的在线计算、随机起点与停止规则的相互作用，以及线性和非线性自回归结果各自所需的条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未给出抽样成本、样本规模及事件识别精度的具体结果。
原文摘要
Seismic data contain complex temporal information that arrives at high speed and has a large, even potentially unbounded volume. The explosion of temporally correlated streaming data from advanced seismic sensors poses analytical challenges due to its sheer volume and real-time nature. Sampling, or data reduction, is a natural yet powerful tool for handling large streaming data while balancing estimation accuracy and computational cost. Currently, data reduction methods and their statistical properties for streaming data, especially streaming autoregressive time series, are not well-studied in the literature. In this article, we propose an online leverage-based sequential data reduction algorithm for streaming autoregressive time series with application to seismic data. The proposed Sequential Leveraging Sampling (SLS) method selects only one consecutively recorded block from the data stream for inference. While the starting point of the SLS block is chosen using a random mechanism based on streaming leverage scores of data, the block size is determined by a sequential stopping rule. The SLS block offers efficient sample usage, as evidenced by our results confirming asymptotic normality for the normalized least squares estimator in both linear and nonlinear autoregressive settings. The SLS method is applied to two seismic datasets: the 2023 Turkey-Syria earthquake doublet data on the macroseismic scale and the Oklahoma seismic data on the microseismic scale. We demonstrate the ability of the SLS method to efficiently identify seismic events and elucidate their intricate temporal dependence structure. Simulation studies are presented to evaluate the empirical performance of the SLS method.