时间序列的变化点检测和趋势分析

趋势分析和变化点检测是时间序列分析中常用的工具. 变化点检测是识别过程行为的自然或人为的突然的变化，而趋势可以定义为对逐渐偏离过去的规范的估计. 本文使用了Cox-Stuart方法和变化点算法分析时间序列数据趋势的存在，并以澳大利亚的近地表风速时间序列为例. 澳大利亚的近地表风速趋势是根据研究出的新开发的风速数据集，通过使用局部表面粗糙度信息，以及不同高度收集的混合观测数据构建. 10 m处的风的速度趋势通常会增加，而2 m处则趋于减小. 假设检验测试，变化点分析和人工检查记录表明有几个因素可能是导致差异的原因，例如伴随仪器变化的系统性偏差，随机数据错误(例如累积日错误)和数据采样问题. 均质化以及基于变化点检测的技术和多期趋势分析阐明了风速趋势不一致的根源.

Change Point Detection and Trend Analysis for Time Series

Trend analysis and change point detection in a time series are frequent analysis tools. Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes, whereas trend can be defined as estimation of gradual departure from past norms. We analyze the time series data in the presence of trend, using Cox-Stuart methods together with the change point algorithms. We applied the methods to the nearsurface wind speed time series for Australia as an example. The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets, which were constructed by blending observational data collected at various heights using local surface roughness information. The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing. Significance testing, change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy, such as systematic biases accompanying instrument changes, random data errors (e.g. accumulation day error) and data sampling issues. Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.