| The Application of Kernel Smoothing to Time Series Data |
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| 作者姓名： | Zhao-jun Wang Yi Zhao Chun-jie Wu Yan-ting Li |
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| 作者单位： | School of Mathematics, Nankai University, Tianjin 300071, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China （No.10371034）. |
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| 摘 要： | There are already a lot of models to fit a set of stationary time series, such as AR, MA, and ARMA models. For the non-stationary data, an ARIMA or seasonal ARIMA models can be used to fit the given data. Moreover, there are also many statistical softwares that can be used to build a stationary or non-stationary time series model for a given set of time series data, such as SAS, SPLUS, etc. However, some statistical softwares wouldn＇t work well for small samples with or without missing data, especially for small time series data with seasonal trend. A nonparametric smoothing technique to build a forecasting model for a given small seasonal time series data is carried out in this paper. And then, both the method provided in this paper and that in SAS package are applied to the modeling of international airline passengers data respectively, the comparisons between the two methods are done afterwards. The results of the comparison show us the method provided in this paper has superiority over SAS＇s method.
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| 关 键 词： | 非参数衰退 核平滑 周期时间序列 ARIMA模型 SAS包 数学分析 |
| 收稿时间： | 2002-08-21 |
| 修稿时间： | 2002-08-212005-03-04 |
The Application of Kernel Smoothing to Time Series Data |
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| Zhao-jun Wang Yi Zhao Chun-jie Wu Yan-ting Li.The Application of Kernel Smoothing to Time Series Data[J].Acta Mathematicae Applicatae Sinica,2006,22(2):219-226. |
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| Authors: | Zhao-jun Wang Yi Zhao Chun-jie Wu Yan-ting Li |
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| Institution: | (1) School of Mathematics, Nankai University, Tianjin 300071, China |
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| Abstract: | There are already a lot of models to fit a set of stationary time series,such as AR,MA,and ARMA models.For the non-stationary data,an ARIMA or seasonal ARIMA models can be used to fit the given data. Moreover,there are also many statistical softwares that can be used to build a stationary or non-stationary time series model for a given set of time series data,such as SAS,SPLUS,etc.However,some statistical softwares wouldn't work well for small samples with or without missing data,especially for small time series data with seasonal trend.A nonparametric smoothing technique to build a forecasting model for a given small seasonal time series data is carried out in this paper.And then,both the method provided in this paper and that in SAS package are applied to the modeling of international airline passengers data respectively,the comparisons between the two methods are done afterwards.The results of the comparison show us the method provided in this paper has superiority over SAS's method. |
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| Keywords: | Nonparametric regression kernel smoothing seasonal time series ARIMA model SAS package international airline passengers |
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