英文标题：
《Forecasting time series with structural breaks with Singular Spectrum
  Analysis, using a general form of recurrent formula》
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作者：
Donya Rahmani, Saeed Heravi, Hossein Hassani, Mansi Ghodsi
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最新提交年份：
2016
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英文摘要：
  This study extends and evaluates the forecasting performance of the Singular Spectrum Analysis (SSA) technique using a general non-linear form for the re- current formula. In this study, we consider 24 series measuring the monthly seasonally adjusted industrial production of important sectors of the German, French and UK economies. This is tested by comparing the performance of the new proposed model with basic SSA and the SSA bootstrap forecasting, especially when there is evidence of structural breaks in both in-sample and out-of-sample periods. According to root mean-square error (RMSE), SSA using the general recursive formula outperforms both the SSA and the bootstrap forecasting at horizons of up to a year. We found no significant difference in predicting the direction of change between these methods. Therefore, it is suggested that the SSA model with the general recurrent formula should be chosen by users in the case of structural breaks in the series.
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中文摘要：
本研究利用回流公式的一般非线性形式，扩展并评估了奇异谱分析（SSA）技术的预测性能。在这项研究中，我们考虑了24个系列，衡量德国、法国和英国经济体重要部门经季节性调整的月度工业生产。通过将新提出的模型与基本SSA和SSA bootstrap预测的性能进行比较，尤其是在样本期内和样本期外都存在结构性中断的情况下，验证了这一点。根据均方根误差（RMSE），在长达一年的时间范围内，使用通用递归公式的SSA优于SSA和bootstrap预测。我们发现这些方法在预测变化方向方面没有显著差异。因此，建议用户在序列中出现结构突变的情况下，选择具有通用递推公式的SSA模型。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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