为提高光伏发电功率预测精度,提出一种基于外生因素及季节性的差分自回归移动平均SARIMAX(seasonal autoregressive integrated moving average with exogenous factors)并结合优化支持向量回归SVR(support vector regression)的光伏发...为提高光伏发电功率预测精度,提出一种基于外生因素及季节性的差分自回归移动平均SARIMAX(seasonal autoregressive integrated moving average with exogenous factors)并结合优化支持向量回归SVR(support vector regression)的光伏发电功率预测方法。首先,采用相关性特征法聚类气象条件中关键气象因子,以消除数据冗余并降低ARIMAX模型的复杂性;其次,在ARIMAX模型中引入季节性因素,构建SARIMAX模型来捕捉数据的季节性变化;最后,使用SARIMAX模型的拟合残差其作为SVR模型的输入,进一步拟合数据的非线性。通过仿真算例分析表明,所提方法可显著提高光伏发电功率预测精度。展开更多
Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.展开更多
本文收集了广州地区2003年至2022年的中医药卫生技术人员和医院床位数等数据,采用R语言构建自回归整合移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)进行中医药卫生资源配置预测研究,分析了广州市中医药卫生资...本文收集了广州地区2003年至2022年的中医药卫生技术人员和医院床位数等数据,采用R语言构建自回归整合移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)进行中医药卫生资源配置预测研究,分析了广州市中医药卫生资源的情况以及发展趋势,为广州市相关中医药卫生政策制定提供参考依据。展开更多
目的:探讨基于R语言构建的自回归滑动平均模型(autoregressive integrated moving average model,ARIMA)对医用耗材消耗量的预测效果。方法:选取某类预冲式冲管注射器2018年7月至2023年6月月度消耗量数据作为样本数据,利用R语言对样本...目的:探讨基于R语言构建的自回归滑动平均模型(autoregressive integrated moving average model,ARIMA)对医用耗材消耗量的预测效果。方法:选取某类预冲式冲管注射器2018年7月至2023年6月月度消耗量数据作为样本数据,利用R语言对样本数据进行平稳性检验、差分运算等处理,根据赤池信息准则和贝叶斯信息准则,构建ARIMA模型并确定最优模型。以2023年第三季度相应数据作为验证集进行消耗情况预测,并与实际使用情况进行对比,评价ARIMA模型的预测效果。结果:拟合最优的ARIMA模型为ARIMA(0,1,1)(1,0,0)12,预测数据均在95%置信区间,其平均绝对百分比误差为9.92%,使用Ljung-Box统计量对残差序列进行检验时P>0.05,预测结果较为理想。结论:基于R语言的ARIMA模型对医用耗材消耗量预测效果较好,为医用耗材的需求计划制订、预算、采购、管理等工作提供了参考。展开更多
The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the ...The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.展开更多
目的:建立适用于苏州市区临床红细胞类血液需求预测的自回归移动平均模型(autoregressive integrated moving average model,ARIMA),从需求出发指导采供血机构对地区血液资源进行合理采集、科学调配。方法:收集区域临床用血历史数据,采...目的:建立适用于苏州市区临床红细胞类血液需求预测的自回归移动平均模型(autoregressive integrated moving average model,ARIMA),从需求出发指导采供血机构对地区血液资源进行合理采集、科学调配。方法:收集区域临床用血历史数据,采用时间序列分析方法,选取苏州市区2009-2019年每月红细胞类成分血的临床使用数据,运用SPSS 26软件进行数据分析和ARIMA模型构建,通过模型识别、参数估计及最优模型检验,确定临床红细胞类血液预测的最优模型。运用所得最优模型对2020年1-11月红细胞类成分血临床用量进行预测,将预测值与实际数值对比,验证模型预测效果。结果:最优模型为ARIMA(0,1,1)(0,1,1)12,残差的ACF自相关函数值和PACF偏自相关函数值均在95%CI内,同时杨-博克斯Q统计量值为17.992,P>0.05,残差序列不存在自相关,通过白噪声检验。对2020年1-11月苏州市区红细胞类成分血临床用量进行预测,预测值与实际值曲线趋势基本相同,且预测值均在95%CI内,平均相对误差较小,为8.21%,模型预测效果较好。结论:苏州地区临床红细胞类血液需求预测研究的最优模型为ARIMA(0,1,1)(0,1,1)12。展开更多
文摘为提高光伏发电功率预测精度,提出一种基于外生因素及季节性的差分自回归移动平均SARIMAX(seasonal autoregressive integrated moving average with exogenous factors)并结合优化支持向量回归SVR(support vector regression)的光伏发电功率预测方法。首先,采用相关性特征法聚类气象条件中关键气象因子,以消除数据冗余并降低ARIMAX模型的复杂性;其次,在ARIMAX模型中引入季节性因素,构建SARIMAX模型来捕捉数据的季节性变化;最后,使用SARIMAX模型的拟合残差其作为SVR模型的输入,进一步拟合数据的非线性。通过仿真算例分析表明,所提方法可显著提高光伏发电功率预测精度。
基金financially supported by the Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
文摘本文收集了广州地区2003年至2022年的中医药卫生技术人员和医院床位数等数据,采用R语言构建自回归整合移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)进行中医药卫生资源配置预测研究,分析了广州市中医药卫生资源的情况以及发展趋势,为广州市相关中医药卫生政策制定提供参考依据。
文摘目的:探讨基于R语言构建的自回归滑动平均模型(autoregressive integrated moving average model,ARIMA)对医用耗材消耗量的预测效果。方法:选取某类预冲式冲管注射器2018年7月至2023年6月月度消耗量数据作为样本数据,利用R语言对样本数据进行平稳性检验、差分运算等处理,根据赤池信息准则和贝叶斯信息准则,构建ARIMA模型并确定最优模型。以2023年第三季度相应数据作为验证集进行消耗情况预测,并与实际使用情况进行对比,评价ARIMA模型的预测效果。结果:拟合最优的ARIMA模型为ARIMA(0,1,1)(1,0,0)12,预测数据均在95%置信区间,其平均绝对百分比误差为9.92%,使用Ljung-Box统计量对残差序列进行检验时P>0.05,预测结果较为理想。结论:基于R语言的ARIMA模型对医用耗材消耗量预测效果较好,为医用耗材的需求计划制订、预算、采购、管理等工作提供了参考。
文摘The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.
