本文采用时间序列方法中的 AR 模型和非平稳时间序模型对云锡老矿各年代下井矿工的肺癌死亡进行了预测,得到了1936～1995年期间各组矿工肺癌死亡的预测情况,并在此基础上进行了分析,对解放后所采取的防护、防治措施的奏效程度进行了评价...本文采用时间序列方法中的 AR 模型和非平稳时间序模型对云锡老矿各年代下井矿工的肺癌死亡进行了预测,得到了1936～1995年期间各组矿工肺癌死亡的预测情况,并在此基础上进行了分析,对解放后所采取的防护、防治措施的奏效程度进行了评价,对云锡矿工肺癌的发病趋势作了宏观和微观的结论,是一篇综合性的论文报告。展开更多
Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used t...Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.展开更多
文摘本文采用时间序列方法中的 AR 模型和非平稳时间序模型对云锡老矿各年代下井矿工的肺癌死亡进行了预测,得到了1936～1995年期间各组矿工肺癌死亡的预测情况,并在此基础上进行了分析,对解放后所采取的防护、防治措施的奏效程度进行了评价,对云锡矿工肺癌的发病趋势作了宏观和微观的结论,是一篇综合性的论文报告。
基金Projects(70671039,71071052) supported by the National Natural Science Foundation of ChinaProjects(10QX44,09QX68) supported by the Fundamental Research Funds for the Central Universities in China
文摘Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.
