Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

时序模型ARIMA在数据分析中的应用

时间序列是进行趋势分析的方法之一。随着大数据时代的到来,经济趋势、企业经营、市场预测和天气预测等常常需要进行预测和分析。本文对某知名化妆品公司2010年至2018年间的2122条股票数据,采用ARIMA模型进行趋势分析,预测未来的发展趋势。通过模型的拟合与效果考核,所得到的结果说明了应用ARIMA模型对股票进行趋势分析时,可以取得较好的预测效果。

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。

Time series modeling of animal bites

Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017.We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average(ARIMA)model to fit a univariate time series of animal bite incidence.The ARIMA modeling process involved selecting the time series,transforming the series,selecting the appropriate model,estimating parameters,and forecasting.Results:Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period.The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA(2,0,0)(1,1,1).This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years,suggesting that the incidence of animal attacks in the region would continue to increase during this time frame(2018-2019).Conclusion:Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences.The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year.Therefore,our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran.

基于ARIMA-LSTM混合模型对传染病的预测分析

传染病一直是科学研究的热点,利用科学的方法控制传染病的传播对整个国家乃至全世界具有举足轻重的作用。文章选取乙类传染病中新型冠状病毒感染数据作为研究对象,搜集了北京市2022年1月至2022年4月新冠感染累计确诊病例数,构成时间序列,基于自回归移动平均模型(ARIMA)和长短期记忆神经网络(LSTM)的混合模型进行预测分析。结果表明,混合模型的预测结果与实际情况基本一致。

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Time Series Analysis on Selected Rainfall Stations Data in Louisiana Using ARIMA Approach

Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)12), Abbeville (ARMA (0,0,1) (0,0,2)12), Monroe Regional (ARMA (0,0,1) (0,0,0)12), New Orleans Airport (ARMA (1,0,0) (0,0,2)12), Alexandria (ARMA (1,0,1) (0,0,0)12), Logansport (ARIMA (0,1,2) (0,0,0)12), New Orleans Audubon (ARMA (1,0,0) (0,0,0)12), Lake Charles Airport (ARMA (2,0,2) (0,0,0)12) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.

基于ARIMA构建SWECPX模型解决电商需求预测问题

本文针对电商需求预测问题,基于促销节日因素S(Sale)和仓库因素C(Category),借助Matlab、Excel软件进行数据预处理,以ARIMA时间序列模型为核心,建立SWECPX(Sale Ware Effect Category Product X)模型,使用Matlab软件中的X-12-ARIMA选项等方法进行求解,实现了对商品需求量的准确预测,取得较好的1-wrmape指标测试效果。本文最大的创新点是提出了SWECPX模型,对影响商品需求量的要素S和C进行区分和求解,使对商品需求量预测更加精确,1-wrmape值较高。当每日的商品需求量处于较低水平时,预测效果的提升尤为显著,其预测值几乎与实际值相同。因此,我们期望SWECPX模型可以为电商仓储平台的决策提供切实的参考和借鉴。

基于ARIMA-GM模型的矿井涌水量预测

在煤矿开采过程中,矿井涌水量预测对矿井安全生产具有重要意义。以新疆苇子沟煤矿为例,前期采用比拟法、水平廊道法等传统方法预测矿井涌水量误差较大,基于灰色系统理论与时间序列分析,采用GM(1,1)灰色模型和时间序列ARIMA模型分别对首采区涌水量进行预测,通过平均相对误差确定权重进而建立ARIMA-GM组合模型,对比分析涌水量预测结果与实测矿井涌水量。研究结果表明:采用ARIMA-GM组合模型预测首采区涌水量时,涌水量预测值为28.45 m^(3)/h,历年实测值与模型预测值的平均相对误差仅为5.91%,模型拟合优度较好,预测精度较高。ARIMA-GM模型可以满足矿井涌水量短期预测的需要,并为苇子沟煤矿防治水工作提供参考依据。

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

基于LSTM-ARIMA模型的隧道围岩变形预测方法研究

为了在隧道施工过程中对隧道围岩变形进行精准预测,以中东某隧道为研究对象,基于隧道围岩监测数据使用LSTM(long short-term memory)和ARIMA(autoregressive integrated moving average)模型进行拟合并预测,进一步通过方差倒数法建立组合模型,采用多个统计学指标对建立的模型预测结果进行对比分析。结果表明,组合模型解决了ARIMA模型对非平稳数据预测精度较差的问题,充分发挥了LSTM和ARIMA模型各自的优势,能够更准确地捕捉数据特征和趋势,提高了对隧道围岩变形预测的准确性和鲁棒性,可为隧道工程的安全施工提供可靠的支持和指导。

基于ARIMA和模拟退火算法的电商包裹调运问题研究

随着网络购物方式的日益普及和互联网经济的发展,因促销活动和物流场地停用所导致的运输和分拣包裹成本上升的问题也随之而来。为了降低运营成本、提高运营效率,预测物流场地和运输线路的货物量,为货物调运选择最优路线和方案尤为重要。基于此,建立了ARIMA时间序列预测模型,对物流场地和线路未来的货运量进行预测研究,建立线性规划模型,优化调整突发情况下的货运线路,利用模拟退火算法进行求解,选择影响程度较小的最优线路,降低运输成本。

融合情绪分析和Informer-ARIMA模型的比特币价格预测方法

相较于传统金融产品,比特币价格更容易受到情绪的影响而展现出更高的波动性,为此价格预测具有极高的研究价值。为提高比特币价格预测的精准度,文章在预测模型中引入情绪指标,构建融合情绪分析和Informer-ARIMA模型的预测方法。从多维度分析价格时间序列的随机波动、循环变化、周期变化等变化规律,对比特币的价格进行有效预测。测试结果表明,融合情绪分析的Informer-ARIMA模型性能更优,验证了所提方法的可行性和有效性。

Selection of Heteroscedastic Models: A Time Series Forecasting Approach

To overcome the weaknesses of in-sample model selection, this study adopted out-of-sample model selection approach for selecting models with improved forecasting accuracies and performances. Daily closing share prices were obtained from Diamond Bank and Fidelity Bank as listed in the Nigerian Stock Exchange spanning from January 3, 2006 to December 30, 2016. Thus, a total of 2713 observations were explored and were divided into two portions. The first which ranged from January 3, 2006 to November 24, 2016, comprising 2690 observations, was used for model formulation. The second portion which ranged from November 25, 2016 to December 30, 2016, consisting of 23 observations, was used for out-of-sample forecasting performance evaluation. Combined linear (ARIMA) and Nonlinear (GARCH-type) models were applied on the returns series with respect to normal and student-t distributions. The findings revealed that ARIMA (2,1,1)-EGARCH (1,1)-norm and ARIMA (1,1,0)-EGARCH (1,1)-norm models selected based on minimum predictive errors throughout-of-sample approach outperformed ARIMA (2,1,1)-GARCH (2,0)-std and ARIMA (1,1,0)-EGARCH (1,1)-std model chosen through in-sample approach. Therefore, it could be deduced that out-of-sample model selection approach was suitable for selecting models with improved forecasting accuracies and performances.

基于ARIMA模型的致密气田气井产量预测

由于天然气井日产量数据存在随机的关井操作,是不可预测的人为因素,为克服人为因素造成的误差,以实际开井生产数据为基础,将累计产气量作为时间序列,基于ARIMA模型对鄂尔多斯盆地致密气田S区块44口生产井使用差分自回归移动平均模型建模,将以前的累产序列为训练集,预测300 d的累计产气量,交叉验证的结果显示:①ARIMA模型为线性模型,对于生产稳定的气井累计产气量拟合效果较好,方法简单,预测精度高,对于产量断崖式变化的单井预测效果较差,而对于以区块为单位的累计产量预测误差小,和实际生产数据拟合效果好,有应用价值;②以传统的自相关函数和偏相关函数拖尾和截尾的特征进行参数优化很难确定最优化参数,而且人为因素较大,在确定当前时间产气量受历史数据影响的最大阶数后,采用遍历的方法建立模型,以赤池信息准则和贝叶斯信息准则值最小作为模型选择的策略,预测的S区块300 d的累计产量和实际产量误差分别为0.40%(AIC最小)和2.11%(BIC最小),满足产气量预测的精度;③从S区块44口井的统计结果来看,在满足差分后数据序列稳定的前提下,单独一阶差分和二阶差分预测的数据序列偏离较大,为消除随机误差,文中取一阶差分和二阶差分的平均值作为预测的最终结果,预测效果明显提升。

基于ARIMA模型对定西天气数据的分析与预测

由于天气对农业生产、水资源管理和自然灾害预防等具有重要影响,文章采用ARIMA模型来实现对天气的有效预测。通过利用ACF和PACF图粗略确定ARIMA模型的参数,最终确定最优模型:ARIMA(1,1,1)为日最低气温模型,其残差序列自相关函数与偏自相关函数基本落在95%置信区间内;同时Ljung-Box Q统计结果表明残差不存在相关关系(P>0.05),即残差为白噪声,满足随机性假设;最终计算误差(日最低气温)RMSE、MAPE、MAE分别为2.63、1.22%、2.06,预测结果良好,为定西天气的预测提供了可行的方案。

基于数据预处理的ARIMA模型超短期风电功率预测

为了提高超短期风电功率预测的精确度和稳定性,文章提出了基于预处理数据的ARIMA时间序列自回归差分移动平均模型风电超短期预测算法。以黑龙江省某风电场的实测数据为例,对测风塔数据进行预处理,对风电场数据异常值进行处理,并对风电功率影响因素相关性进行分析,对所得到的数据进行差分处理,从而适应ARIMA模型的预测。结果表明,此方法可以有效提高预测精度和覆盖率。

基于ARIMA-LSTM的高速公路交通安全组合预测模型研究

为建立准确有效的交通事故预测模型,提升高速公路交通安全水平,以重庆市11条高速公路2011—2016年共计65 119起交通事故为基础,选取“事故数量”和“死亡人数”2项总量指标,描述统计高速公路交通事故在时间维度上的月分布规律。通过自回归差分移动平均(ARIMA)模型捕捉时间序列数据中的线性时序特征,使用长短时记忆神经网络(LSTM)模型拟合预测残差序列中的非线性时序特征,建立了基于ARIMA和LSTM的高速公路交通事故组合预测模型,并以均方根误差(RMSE)、平均绝对百分比误差(MAPE)值作为模型的评估指标。结果表明:ARIMA-LSTM组合预测模型各项指标的预测精度均优于单一的ARIMA模型,其中“死亡人数”组合模型改善效果显著,其RMSE与MAPE值相较于ARIMA模型分别改善了55.83%和54.80%;“事故数量”组合模型的RMSE和MAPE相较于ARIMA模型改善了23.15%、23.29%。

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

基于ARIMA模型的医院流感样病例特性分析与预测研究

目的基于自回归移动平均模型分析预测医院流感样病例就诊特点,探讨疫情防控应急管理策略。方法采用回顾性研究收集某三甲医院2017-2022年流感样病例,分析不同季度、科室流感样病例流行病学特征,采用时间序列拟合模型分析流感样病例流行趋势及变化。结果2017—2022年医院就诊的5048123例门急诊病例中监测到118781例流感样病例,其中2017—2019年12月至次年3月呈现出流感样病例发病高峰,2020—2022年由于新冠疫情防控措施使得流感样病例就诊未呈现典型的季节性波动。采用时间序列拟合模型分析后,预测2023—2024年流感样病例及门急诊病例数,预测季节性高峰将于2023年12月、2024年3月以及2024年12月出现。结论利用ARIMA模型对流感样病例进行分析预测有助于医院统筹医疗资源,加强应急管理,降低流感传播风险。