Simulation study of singular spectrum analysis from time series data with outlier
Simulation study of singular spectrum analysis from time series data with outlier
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DOI：
10.1088/1757-899x/434/1/012068
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
G. Darmawan;Dedi Rosadi;B. N. Ruchjana
中科院分区：
文献类型：
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作者：
G. Darmawan;Dedi Rosadi;B. N. Ruchjana
在本模拟研究中，奇异谱分析（SSA）将用于分析具有异常值的时间序列数据。具有异常值的时间序列数据将由开源软件 R (OSSR) 生成，用于多次复制。本研究的目的是评估 SSA 模型的稳健性。 SSA 分析两种类型的时间序列数据，包括异常值和无异常值。 S SA的性能将通过改变SSA参数（窗口长度）来评估，其他参数由自动分组确定。此外，还针对两种类型的数据测量了 SSA 的 MAPE（平均绝对百分比误差）。模拟研究的结果表明，数据越大，异常值的影响越小。然而，数据越大，L 值的偏移越大。如果移动平均 (MA) 数据中存在可用的异常值，SSA 的精度将降低 0.245 (MAPE)。对于自回归数据，如果时间序列数据中存在可用的单个异常值，SSA 的准确性将降低 0.264 (MAPE)。
In this simulation study, Singular Spectrum Analysis (SSA) will be used to analyze time series data with outlier. Time series data with outliers will be generated by open source software R (OSSR) for many replications. The goal of this research is to evaluate robustnes of the SSA model. SSA analyzes both types of time series data, with outlier and without outlier. Performance of S SA will be evaluated by changing of SSA parameter (Window length) and other parameter is determined by automatic grouping. Moreover, the MAPE (mean absolute percentage Error) of SSA is measured for both types of data. The result of Simulation study shows the larger the data the smaller the effect of the outliers. However, the larger the data the greater the shift in the value of L. The accuracy of SSA will decrease by 0.245 (MAPE) if there is available outlier in Moving Average (MA) data. For Autoregressive data, the accuracy of SSA will decrease by 0.264 (MAPE) if there is available single outlier in time series data.