Rollage：一种有效的时间序列数据ARMA模型估计的滚动平均算法
Rollage: Efficient Rolling Average Algorithm to Estimate ARMA Models for Big Time Series Data
摘要：
本文提出了一种新的有效算法，用于分析大规模时间序列数据。我们首先定义了滚动平均数，推导了它们的分析性质，并建立了它们的渐近分布。这些理论结果随后被利用开发一个有效的算法，称为滚动，适合一个适当的AR模型大时间序列数据。当与Durbin算法结合使用时，我们表明，Rollage算法可以作为一个标准，以最佳拟合ARMA模型大时间序列数据。大规模的合成时间序列数据的实证实验支持的理论结果，并揭示了这种新方法的有效性，特别是当与现有的方法相比。
英文摘要：
We develop a new efficient algorithm for the analysis of large-scale time series data. We firstly define rolling averages, derive their analytical properties, and establish their asymptotic distribution. These theoretical results are subsequently exploited to develop an efficient algorithm, called Rollage, for fitting an appropriate AR model to big time series data. When used in conjunction with the Durbin's algorithm, we show that the Rollage algorithm can be used as a criterion to optimally fit ARMA models to big time series data. Empirical experiments on large-scale synthetic time series data support the theoretical results and reveal the efficacy of this new approach, especially when compared to existing methodology.
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