针对复杂的应用环境下,时间序列建模不易准确,多步预测精度不高的问题,提出基于粒子滤波(particle filter,PF)优化的滚动式时间序列(roll time series,RTS)多步预测算法(PF_RTS)。采用Box-Jenkins方法对时间序列滚动自适应建模,所建模...针对复杂的应用环境下,时间序列建模不易准确,多步预测精度不高的问题,提出基于粒子滤波(particle filter,PF)优化的滚动式时间序列(roll time series,RTS)多步预测算法(PF_RTS)。采用Box-Jenkins方法对时间序列滚动自适应建模,所建模型作为粒子的状态转移方程,利用粒子滤波算法实时动态修正预测数据,逼近状态的最优估计。本文算法具有自学习能力,适合实时应用。仿真结果表明,本文算法需要的先验知识少,提高了预测的精度。展开更多
为分析青藏铁路路基高程不规则变形,基于Box-Jenkins建模方法,确定时间序列模型阶数,根据AIC(Akaike information criterion)准则,选取适合的时间序列模型,最后给出批量预测全部路基测点高程的算法步骤。通过建立高程-时间响应模型的方...为分析青藏铁路路基高程不规则变形,基于Box-Jenkins建模方法,确定时间序列模型阶数,根据AIC(Akaike information criterion)准则,选取适合的时间序列模型,最后给出批量预测全部路基测点高程的算法步骤。通过建立高程-时间响应模型的方法,研究了青藏铁路路基高程随时间变形规律问题。以2010—2018年每月青藏铁路K1425+050处左侧路基高程数据为例,建立了ARIMA(2,1,1)模型,并以2019年数据作为验证集。结果表明:模型通过了模型适应性检验,证明了模型的有效性和准确性;总结了青藏铁路沿线各测点至2023年12月预测值中可能出现重大变形以及测点左右两侧路基高程差值出现较大差值的10个危险点;在测点K1476+600附近,路基两侧出现明显长距离的差异。可见本模型能准确预测青藏铁路路基高程的变化,对于工程养护维修具有一定借鉴意义。展开更多
In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. We consider data of Malaria cases from Ministry of Health (Kabwe District)-Zambia for the period, 2...In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. We consider data of Malaria cases from Ministry of Health (Kabwe District)-Zambia for the period, 2009 to 2013 for age 1 to under 5 years. The model-building process involves three steps: tentative identification of a model from the ARIMA class, estimation of parameters in the identified model, and diagnostic checks. Results show that an appropriate model is simply an ARIMA (1, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag 1 which is an indication of the said model. The forecasted Malaria cases for January and February, 2014 are 220 and 265, respectively.展开更多
文摘针对复杂的应用环境下,时间序列建模不易准确,多步预测精度不高的问题,提出基于粒子滤波(particle filter,PF)优化的滚动式时间序列(roll time series,RTS)多步预测算法(PF_RTS)。采用Box-Jenkins方法对时间序列滚动自适应建模,所建模型作为粒子的状态转移方程,利用粒子滤波算法实时动态修正预测数据,逼近状态的最优估计。本文算法具有自学习能力,适合实时应用。仿真结果表明,本文算法需要的先验知识少,提高了预测的精度。
文摘为分析青藏铁路路基高程不规则变形,基于Box-Jenkins建模方法,确定时间序列模型阶数,根据AIC(Akaike information criterion)准则,选取适合的时间序列模型,最后给出批量预测全部路基测点高程的算法步骤。通过建立高程-时间响应模型的方法,研究了青藏铁路路基高程随时间变形规律问题。以2010—2018年每月青藏铁路K1425+050处左侧路基高程数据为例,建立了ARIMA(2,1,1)模型,并以2019年数据作为验证集。结果表明:模型通过了模型适应性检验,证明了模型的有效性和准确性;总结了青藏铁路沿线各测点至2023年12月预测值中可能出现重大变形以及测点左右两侧路基高程差值出现较大差值的10个危险点;在测点K1476+600附近,路基两侧出现明显长距离的差异。可见本模型能准确预测青藏铁路路基高程的变化,对于工程养护维修具有一定借鉴意义。
文摘In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. We consider data of Malaria cases from Ministry of Health (Kabwe District)-Zambia for the period, 2009 to 2013 for age 1 to under 5 years. The model-building process involves three steps: tentative identification of a model from the ARIMA class, estimation of parameters in the identified model, and diagnostic checks. Results show that an appropriate model is simply an ARIMA (1, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag 1 which is an indication of the said model. The forecasted Malaria cases for January and February, 2014 are 220 and 265, respectively.
