Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Development of temporal modeling for prediction of dengue infection in Northeastern Thailand

Objective:To model the monthly number of dengue fever cases in northeastern Thailand using time series analysis.Methods:Autoregressive Integrated Moving Average(ARIMA) models have been developed on the monthly data collected from January 1981 to December 2006 and validated using the data from January 2007 to April 2010.Results:The ARIMA(3,1,4) model has been found as the most suitalile model with the least Akaike Information Criterion(AIC) of 14.060 and Mean Absolute Percent Error(MAPE) of 7.000.The model was fiuther validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times.Conclusions: Early warning based on the data in the previous months could assist in improving vector control, community intervention,and personal protection.

Online Fault Prediction Based on Combined AOSVR and ARMA Models

Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always offline that are not suitable for online and real-time processing. For the complicated nonlinear and non-stationary time series, it is hard to achieve exact predicting result with single models such as support vector regression （SVR）, artifieial neural network （ANN）, and autoregressive moving average （ARMA）. Combined with the accurate online support vector regression （AOSVR） algorithm and ARMA model, a new online approach is presented to forecast fault with time series prediction. The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes. Moreover, its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model. Fault prediction with combined AOSVR and ARMA can be realized better than with the single one. Experiments on Tennessee Eastman process fault data show the new method is practical and effective.

Research on Hydrological Time Series Prediction Based on Combined Model

Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.

计及SOC自恢复的混合储能平抑风电功率波动控制

混合储能系统能够较好地应对复杂的风电波动,有效地提高电网的稳定性和安全性。在混合储能平抑风电功率波动的典型应用场景下,该文首先提出一种计及荷电状态(SOC)自恢复的混合储能平抑风电功率波动控制方法,在满足风电平抑需求的情况下,通过模型预测控制快速调节储能在平抑功率过程中的荷电状态,提高储能持续稳定运行能力;然后,为提高混合储能系统协调运行能力,设计了加权滑动平均(WMA)-模糊控制策略对超级电容和蓄电池功率进行动态分配;最后,结合实际风电功率数据,通过仿真验证了所提策略能有效平衡储能寿命和平抑风电波动的矛盾,能充分考虑两种储能设备的特性差异并提高功率分配的合理性。

基于改进JRD及误差修正的轴承剩余寿命预测方法

目前,风电机组齿轮箱性能发生初始退化时难以识别,现有退化指标易出现剧烈波动、单调性较差,且无法准确预测齿轮箱关键部件如轴承的剩余使用寿命(RUL),针对该问题,提出了一种基于改进杰森-瑞丽散度(JRD)及误差修正的双指数模型轴承RUL预测方法。首先,提取了振动信号样本的多域特征指标,利用高斯混合模型(GMM)与指数型权重JRD,得到了样本的后验概率分布向量,再经归一化处理得到置信值(CV);然后,对轴承从初始健康状态退化至当前检查时刻的CV值进行了相空间重构,提取了CV序列的动力学特征,并将其作为相关向量机(RVM)的训练集,获得了支撑整个退化轨迹的相关向量;最后,利用双指数模型拟合了相关向量,外推趋势至失效门限以计算RUL,并引入了差分整合移动平均自回归模型(ARIMA),对拟合相关向量产生的拟合误差进行了预测,以修正预测的结果。实验结果表明:改进后的退化指标单调性指标提高14.3%;且在不同工况、不同时刻下,经误差修正后的轴承的RUL预测结果较未修正之前有明显提高。研究结果表明:该预测方法可为风电机组齿轮箱重要部件的预测性维护提供参考。

基于SARIMA和SVR组合模型的转向架系统寿命评估

随着地铁运营时间和里程的增加,地铁车辆逐渐接近其理论寿命,为确保车辆运行安全性,需对其重要子系统进行健康状态及剩余寿命评估。文中选取车辆转向架系统作为研究对象,提出了一种基于协方差优选法的季节性回归移动平均(SARIMA)和支持向量回归(SVR)的组合模型对转向架寿命进行评估。首先,将车辆转向架系统历史故障率转化为健康指数,然后基于协方差优选法将SARIMA和SVR进行赋权组合,根据转向架系统历史健康指数进行预测,最后建立历史和预测的健康指数与运行时间的数学模型,分析得到转向架系统的剩余寿命。以某地铁车辆转向架系统为例进行算例分析及验证,结果表明组合模型可更准确地预测其健康状态,为有关维修部门开展维修维护策略提供理论依据,估计得出其剩余寿命,为车辆寿命后期退役及延寿决策提供理论数据分析支撑。

基于季节ARIMA模型对某三级综合性医院门诊量的预测研究

目的 通过建立季节ARIMA模型,对浙江省某三级综合性医院门诊量进行预测,为医院合理配备门诊人力资源提供依据。方法 以2013年1—6月浙江省某医院门诊量数据为基线,利用SPSS软件构建季节ARIMA模型,对2023年7—12月的门诊量进行预测,通过对比门诊量实测值,评价季节ARIMA模型预测门诊人次的精度。结果 该综合性医院门诊量呈现逐年上升趋势,并呈现周期性波动的特征。拟合的最优季节ARIMA模型为ARIMA(0,1,1)(1,0,1)12,BIC(贝叶斯信息准则)为5.273,MAPE(平均绝对百分误差)为14.265,R2(模块决定系数)为0.408,总体相对误差为1.83%,预测结果良好。结论 季节ARIMA模型较好地模拟了该三级综合性医院门诊量在时间序列上的变化趋势,为该院门诊量的短期预测提供理论依据。

基于CNN-LSTM-ARIMA的超短期风速预测

提升风速预测的精准度对于实时调整电力系统的管理策略及增强风电市场的竞争实力有着关键作用。提出一种基于卷积神经网络(CNN)、长短期记忆网络(LSTM)和自回归集成移动平均(ARIMA)模型的超短期风速预测方法,通过CNN卷积层捕捉时间序列数据中的模式和局部特征,利用LSTM模型对提取的特征进行学习训练,基于CNN-LSTM组合架构模型,预测未来风速并对比实际数据获得残差值,最终利用ARIMA分析历史残差来修正未来的预测误差值,实现对风速的超短期预测。以土耳其某个风电场的实际风速记录为基础,对未来10min的风速进行预测。结果表明,与CNN-LSTM、双层LSTM传统神经网络模型相比,CNN-LSTM-ARIMA模型对风速预测结果的平均绝对误差分别下降了16.40%、26.92%,能显著提高预测精度。

基于ARIMA模型预测镇江市肺结核流行趋势及分析

目的通过构建季节性差分整合移动平均自回归模型(ARIMA模型)预测江苏省镇江市肺结核流行趋势并验证模型的有效性,探讨新型冠状病毒感染疫情对肺结核流行情况的影响。方法收集江苏省镇江市2014-2022年肺结核月发病数资料,构建季节性ARIMA模型,以2022年1-12月肺结核发病数验证预测模型效果,并分析预测误差产生的原因。结果2014-2022年镇江市共报告肺结核病例11316例,除2017、2019年发病率有所回升外,总体发病率呈下降趋势,发病主要集中在3-8月。ARIMA(1,1,1)(1,1,0)_(12)的BIC值(5.913)最小,残差白噪声也通过检验。但短期自相关部分的AR系数不显著,因此建立ARIMA(0,1,1)(1,1,0)_(12)。2022年镇江市肺结核月发病数实际值与预测值存在一定的偏差(平均相对预测误差为19.20%),但均在拟合值的95%可信区间内,实际月发病数(平均78例/月)与预测值(平均78例/月)变化趋势基本一致,模型拟合度较好,可用于预测镇江市肺结核流行情况。结论利用该模型对短期内镇江市肺结核发病数进行预测,认为镇江市肺结核流行总体上仍将长期保持下行趋势。

交叉口车辆行为感知在线半监督混合方法

自动驾驶感知系统须对目标车辆运动进行感知,以制定合理交互决策。针对行为感知在时间上的滞后性和数据中可能存在的波动和异常值导致感知准确率差的问题,本文提出一种在线半监督混合方法。首先,采用自回归积分移动平均和在线梯度下降优化器设计基于数据驱动的车辆运动状态在线预测算法。然后,构建基于微簇的初始模型,并以K近邻为基分类器建立集成学习策略,设计错误驱动代表性学习和指数衰减策略实现对初始模型的迭代更新。最后,基于驾驶模拟平台采集了验证所提算法有效性的实验数据。结果表明,所提出的方法对于车辆行为波动具有快速适应性,在线预测算法可准确预测车辆运动趋势,行为感知算法对于不同预测时间下的车辆行为均有较强适应能力。

基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究

为提高大坝变形预测精度,针对大坝变形数据的复杂性和非线性等特征,基于自适应噪声完备集成经验模态分解(CEEMDAN)、数据处理群集法(GMDH)和差分自回归移动平均模型算法(ARIMA)进行大坝变形预测研究。采用CEEMDAN将大坝变形原始数据分解为高频随机分量、中频周期分量和低频趋势分量,再分别采用GMDH模型、ARIMA模型对高中频分量、低频分量进行预测,建立基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型。以江西上犹江水电站为例,将该模型预测结果与反向传播(BP)、径向基函数(RBF)、GMDH和CEEMDAN-GMDH模型的预测结果进行对比分析。结果表明:CEEMDAN-GMDH-ARIMA模型的均方根误差(E_(RMS))、平均绝对误差(E_(MA))、相关系数(r)分别为0.048 mm、0.035 mm、0.994,均优于BP、RBF、GMDH、CEEMDAN-GMDH模型,模型预测效果最好,能够很好地体现监测点水平位移变化趋势。

基于ARIMA模型的南昌市结核病流行趋势预测分析

目的:分析南昌市肺结核的流行趋势特征,探讨应用自回归移动平均模型对南昌市肺结核疫情流行趋势特征进行分析和预测,为相关部门制定肺结核综合防控策略提供参考依据。方法:通过中国疾病预防控制中心传染病监测系统获取2012年1月1日—2022年12月31日,现住址为南昌市的肺结核报告发病数据,应用SPSS 25.0软件构建基于南昌市肺结核发病数的自回归移动平均模型,对南昌市肺结核疫情的流行趋势进行分析和预测。结果:2012—2022年南昌市共报告新发肺结核病例44049例,总体呈逐年下降趋势。确定最优预测模型为ARIMA(0,1,4)(0,1,2)12,对2023年1—8月肺结核发病数进行预测并与实际值比较分析的结果显示,预测较好。结论:自回归移动平均模型对肺结核疫情预测效果良好,可以作为肺结核疫情短期预测的工具。

基于误差补偿的多模态协同交通流预测模型

交通流量因受周期性特征、突发状况等多重因素影响,现有模型的预测精度无法满足实际要求.对此,本文提出了基于误差补偿的多模态协同交通流预测模型(Multimodal Collaborative traffic flow prediction model based on Error Compensation,MCEC).针对传统预测模型不能兼顾时间序列和协变量的问题,提出基于小波分析的特征拓展方法,该方法引入聚类算法得到节假日标签特征,将拥堵指数、交通事故图、天气信息作为拓展特征,对特征进行多尺度分解.在训练阶段,为达到充分学习各部分数据、最优匹配模型的效果,采用差分整合移动平均自回归模型(Autoreg Ressive Integrated Moving Average Model,ARIMA)、长短期记忆神经网络(Long Short-Term Memory network,LSTM)、限制动态时间规整技术(Dynamic Time Warping,DTW)以及自注意力机制(Self-Attention),设计了多模态协同模型训练.在误差补偿阶段,将得到的相应过程值输入基于支持向量机回归(Support Vector Regression,SVR)的误差补偿模块,对各分量的误差进行学习、补偿,并重构得到预测结果.使用公开的高速公路数据集对MCEC进行验证,在多个时间间隔下对比实验结果表明,MCEC在交通流量预测中的平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)达到17.02%,比LSTM-SVR、ConvLSTM(Convolutional Long Short-Term Memory network)、ST-GCN(Spatial Temporal Graph Convolutional Networks)、MFFB(Multi-stream Feature Fusion Block)、Transformer等预测模型具有更高的预测精度,MCEC模型具有较好的有效性与合理性.

基于SBAS-InSAR与MA-PSO-BP的南京河西地区地表沉降监测及预测分析

针对南京河西地区城市化进展的不断加快及对该地区的沉降预测研究较少的问题,本文提出一种基于小基线集合成孔径雷达干涉测量(SBAS-InSAR)与滑动平均-粒子群优化-反向传播神经网络算法(MA-PSO-BP)的城市地表形变监测及预测模型。利用2020年3月—2022年3月的22景Sentinel-1A升降轨数据对南京河西地区进行沉降监测,获取研究区升降轨形变量,分析河西地区的沉降趋势与成因,并对监测得到的沉降值进行滑动平均插值,将其作为PSO-BP网络模型的样本输入,构建网络预测模型。结果表明,SBAS-InSAR技术能够有效监测城市长时间的沉降,南京河西地区存在不同程度的沉降,沉降速率为-25.3~20.5 mm/a。对比历史沉降研究,沉降趋势由北部向南部扩张,结合SBAS-InSAR沉降监测数据,分别与BP神经网络和PSO-BP神经网络预测模型进行对比,样本数据经过插值后沉降预测模型的精度最高。

复杂天气条件下光伏电站太阳辐射量短期预测

复杂天气条件下,天气变化波动较大;光伏电站传统太阳辐射量预测模型无法很好地处理复杂的非线性关系,存在精度不足的缺陷,给电力系统的保护和并网安全带来了挑战。为了应对这一挑战,建立了一种基于自适应模糊神经网络(adaptive-network-based fuzzy inference systems,ANFIS)的太阳辐射量预测模型。该模型引入了卫星遥感数据作为输入量,以补充传统的气象数据。首先,使用样本熵计算法对复杂天气进行判定;其次,采用自回归移动平均(auto regression integrated moving average,ARIMA)模型,预测未来24 h的云团光学厚度和气溶胶光学厚度这两种关键的卫星遥感数据。结合大气层上界的太阳辐射量和大气平均温度,建立了基于ANFIS的太阳能辐射量预测模型,从而得到未来24 h的太阳能辐射量预测结果。在算例研究中,将ANFIS模型与多层前馈(back propagation,BP)神经网络预测模型、长短期记忆(long short-term memory,LSTM)神经网络预测模型在不同天气类型中的精度进行了对比。结果表明,在简单天气条件下,ANFIS模型、BP模型、LSTM模型的均方根误差分别为0.1122、0.3184、0.2534 W/m^(2),三者相对较小且相差不大;在复杂天气条件中,ANFIS模型的均方根误差为0.8606 W/m^(2),比BP模型和LSTM模型分别降低了4.0396、2.0252 W/m^(2),这说明ANFIS模型在复杂天气条件下表现较好,能够适应具有较强波动性的数据。研究同时表明,在考虑气象数据的基础上计及卫星遥感数据,可将预测的均方根误差降低0.132 W/m^(2),进一步改进了预测精度。

基于LightGBM的集中供热系统预测控制策略研究

针对城市集中供热系统大时滞非线性、换热站负荷预测与调控过度依赖人工经验、系统预测及调控过程不精细、不及时以及系统能耗偏高等现状,提出了一种基于轻梯度提升机(Light Gradient Boosting Machine,LightGBM)的集中供热系统预测控制策略。首先,结合供热系统气候补偿工艺原理,建立了基于LightGBM换热站回水温度预测模型,用以确保回水温度预测目标的及时性与可靠性;其次,设计了基于移动加权平均算法的预测函数,结合PID控制算法实现了集中供热系统的精准负荷预测与高性能调控。实践结果表明,所提出的基于LightGBM的集中供热系统预测控制策略,可及时精准地预测换热站运行负荷且具有更好的超前控制效果,有效地提高了集中供热控制系统的可靠稳定性能。

基于自回归推广模型的海风预测方法分析

为准确预测海上风速和风向数据,提升船舶海上航行的安全性,针对现有海风预测方法存在的预测精度不高的问题,提出一种基于自回归(Auto-Regressive,AR)推广模型的海风预测方法。将风速和风向看作风矢量的2部分,在确定2组数据的依赖性之后,分别采用差分整合移动平均自回归(Auto-Regressive Integrated Moving Average,ARIMA)模型和向量自回归(Vector Auto-Regressive,VAR)模型这2种AR推广模型对风矢量进行预测。试验结果表明,VAR模型的预测结果中有34.17%的数据落在误差允许范围内,而ARIMA模型的预测结果中有61.25%的数据落在误差允许范围内,该方法可供海上风速和风向预测参考。

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province,China

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013–2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.