吉兰丹日降雨数据集:自然界的持久性

S. M. Norrulashikin, F. Yusof, Z. Yusop, I. Kane, Norizzati Salleh, Aaishah Radziah Jamaludin
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引用次数: 0

摘要

有证据表明,一个固定的短期记忆过程遇到偶尔的结构中断可以显示出长期记忆过程或持久性行为的特性,这可能导致极端天气条件。在本章中,我们应用三种技术对吉兰丹地区6个日降雨数据集进行了长记忆测试。结果解释了所有的数据集都表现出长记忆。采用基于普通最小二乘(OLS)的累积和(CUSUM)检验,采用经验波动过程对结构变化进行检验。结果还表明,在所有数据集中都发现了结构变化。然后对数据集进行长记忆测试,这些数据集被细分为各自的break,结果显示子系列遵循与原始系列相同的模式。因此,这表明在数据生成过程(DGP)中存在真正的长记忆,尽管在数据序列中会发生结构断裂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kelantan Daily Rainfall Datasets: Persistence in Nature
Abstract There is evidence that a stationary short memory process that encounters occasional structural break can show the properties of long memory processes or persistence behaviour which may lead to extreme weather condition. In this chapter, we applied three techniques for testing the long memory for six daily rainfall datasets in Kelantan area. The results explained that all the datasets exhibit long memory. An empirical fluctuation process was employed to test for structural changes using the ordinary least square (OLS)-based cumulative sum (CUSUM) test. The result also shows that structural change was spotted in all datasets. A long memory testing was then engaged to the datasets that were subdivided into their respective break and the results displayed that the subseries follows the same pattern as the original series. Hence, this indicated that there exists a true long memory in the data generating process (DGP) although structural break occurs within the data series.
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