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.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

Detecting DDoS Attacks against Web Server Using Time Series Analysis

Distributed Denial of Service （DDoS） attack is a major threat to the availability of Web service. The inherent presence of self-similarity in Web traffic motivates the applicability of time series analysis in the study of the burst feature of DDoS attack. This paper presents a method of detecting DDoS attacks against Web server by analyzing the abrupt change of time series data obtained from Web traffic. Time series data are specified in reference sliding window and test sliding window, and the abrupt change is modeled using Auto-Regressive （AR） process. By comparing two adjacent nonoverlapping windows of the time series, the attack traffic could be detected at a time point. Combined with alarm correlation and location correlation, not only the presence of DDoS attack, but also its occurring time and location can be deter mined. The experimental results in a test environment are illustrated to justify our method.

Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex

The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.

基于Auto-Regressive的河北省旅游接待人数预测研究

旅游人数是发展旅游业的重要指标,对河北省未来接待旅游人数的预测一直受到河北省旅游局的重视。本文通过以1990年~2019年河北省旅游数据为依托,首先,从市场、景区、政策三个方面分析了河北省旅游业现状,然后进行了河北省历年来旅游接待人数数据的平稳性和白噪声检验,分别运用非平稳时间序列的两种残差自回归模型方法(因变量关于时间的回归模型和延迟因变量回归模型)对以往河北省旅游接待人数建立模型。研究结果发现,前者模型拟合效果较好,并对未来旅游人数进行短期预测。最后为促进河北省旅游业的发展提出了一些相关建议。

序列稀疏自回归方法及其在美股做空数据分析上的应用

采用序列稀疏回归的思路来处理向量自回归模型,并设计适用于大规模时间序列数据分析的序列稀疏自回归方法。研究表明:从因子角度刻画向量自回归模型可以有效地将稀疏矩阵估计问题分解成稀疏奇异向量的估计问题,从而极大地提高了计算效率。以1523家美股上市公司1973年1月—2014年12月的做空数据为例,利用此方法探索公司之间的大规模做空关联网络。研究发现:此方法可以有效地恢复股票做空份额(即某一公司的空头股份数量)与股票收益率之间隐藏的关联网络,对于股票风险溢价研究具有一定启发意义。

基于改进灰狼优化与支持向量回归的滑坡位移预测

针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。

基于时间序列和神经网络的电力设备状态异常检测方法

为进一步提高电力设备异常检测方法对设备信息的利用率,发现更多潜在的设备故障,结合大数据分析技术和设备评估技术,提出了一种基于时间序列和神经网络的状态数据异常检测方法。通过时间序列自回归模型和自组织映射神经网络将连续的电力设备数据离散为单个序列,计算状态变量在时间轴上的转移概率,通过状态转移概率和聚类算法快速检测数据异常。通过实验对该方法的有效性进行验证。结果表明,该方法可以快速、有效地检测电力设备异常状态。

DRG支付方式下药径在某三甲医院骨科的实施效果评价

目的 为促进疾病诊断相关分组(DRG)支付改革、推动医院精细化运营管理及临床合理用药提供参考。方法 以我院(安徽医科大学第三附属医院)骨科为研究对象,基于循证药学证据,构建并实施针对该科室DRG病种的药物治疗临床路径(简称“药径”),将符合DRG病种的患者均纳入药径管理,同一DRG病组患者“同病同治”。采用分段回归时间序列模型,分析实施药径管理对我院骨科医疗服务能力、医疗服务效率和医疗服务质量的影响。结果 药径干预时,我院骨科的平均住院日、住院次均费用、药占比、住院次均药费和抗菌药物使用强度均显著缩短/下降,医疗服务收入占比和医嘱合格率显著上升(P<0.05);药径干预后,平均住院日和抗菌药物使用强度均继续下降,医嘱合格率也继续显著上升(P<0.05)。结论 实施药径可提高医疗服务质量,提升医院运营效率,降低医疗费用支出,推动医院精细化管理体系建设。

2015-2021年新疆和田地区新冠疫情前后肺结核发病趋势分析

目的分析新疆和田地区肺结核发病特征及疫情前后发病趋势,为和田地区肺结核防控措施制定和效果评价提供参考依据。方法收集2015-2021年和田地区肺结核报告发病数据,建立连接点回归模型(JPR)和中断时间序列模型(ITS),分别探索肺结核的发病特征及新疆新冠肺炎疫情防控措施对和田地区肺结核发病趋势的影响,并分析不同性别和年龄亚组发病差异。结果JPR模型结果显示,2015-2021年和田地区肺结核报告发病率总体呈先升后降趋势,转折点出现在2018年12月;男性发病率略高于女性,转折点和发病趋势与总体一致;各年龄亚组≥60岁组发病率最高,发病趋势也呈先上升后下降趋势,≤18岁年龄组发病率在2021年6月出现转折点,但趋势无统计学意义(P>0.05),19~59岁组与≥60岁组转折点与总体一致;ITS模型结果显示,自2020年1月起和田地区肺结核发病率明显下降,从2019年的319.28/10万下降到2021年的155.88/10万,同比下降51.16%,月均下降0.049/10万。结论2018年新疆将结核病筛查工作纳入全民健康体检,大量结核病例被发现,和田地区肺结核的报告发病数在2018年12月达到峰值,随后开始下降,而2020年1月起在新疆新冠疫情隔离措施的影响下,报告发病率呈现明显下降。随着疫情结束应关注可能涌现的潜伏结核患者,做好防疫工作。

数字贸易对消费者行为的影响研究

本文选取2022年抽样的淘宝数据进行分析与挖掘,并基于K-means算法对买家进行聚类分析,初步筛选出疑似刷单行为的买家和卖家。在剔除这些用户后,又利用回归分析法分析卖家获得评价、信用评价体系、卖家店铺等级对销量的影响;采用LSTM算法对销量数据的时间序列进行预测;通过Apriori关联规则算法找到买家与卖家和商品之间的关联。其中,在卖家获得评价对销量的影响中,建立奖励函数来描述好评和差评的影响,结果显示奖励函数与销量呈正相关关系。在信用评价体系对销量的影响中,服务和发货对销量的影响较大。卖家店铺等级,则无明显关系。预测的销量数据虽没有较好的结果,但给出了合理的解释。关联结果显示,买家与卖家和商品之间有一定的联系,本研究仅供参考。

中国XCO_(2)浓度时空分布特征及影响因素研究

本文基于GOSAT卫星遥感监测的XCO_(2)数据,分析2011—2020年我国XCO_(2)的时空动态特征,并采用Pearson相关系数以及时空地理加权回归模型,探讨自然和人类活动对中国XCO_(2)分布的影响机制。结果表明:(1)高浓度XCO_(2)在空间上呈现“东南高-西北低”的分布格局,在局部地区存在高浓度聚集现象。除新疆和西藏西部沙漠地区外,低浓度XCO_(2)按照高纬度到低纬度递减,东部与中西部地区浓度差距超过6 g/mL。(2)2011—2020年的XCO_(2)年均变化幅度超过5.5%;月均XCO_(2)在年内呈周期变化,浓度变化速率最高出现在春夏过渡期,且每年夏季都会出现XCO_(2)相对于该年的快速下降过程。(3)XCO_(2)与各影响因素的相关性由高到低排序为:化石燃料燃烧的碳排放、植被、降水以及气温。随着时间的推移,化石燃料燃烧产生的碳排放对XCO_(2)分布的正向影响空间范围逐渐缩小,而气温的影响在影响强度和影响空间范围上均呈现增大趋势。

基于OBD数据采集的驾驶员驾驶平稳性分析

驾驶员驾驶平稳性分析对研究交通安全影响因素起着至关重要的作用。为此提出基于车载自动诊断系统(OBD)采集数据,利用多项式回归进行短时间内车辆速度预测研究。首先分析影响车辆驾驶速度的客观因素,如地势、天气、行驶路径等。其次通过控制客观因素不变,整合有效驾驶速度数据进行多项式回归预测,得到模型的参数。通过真实值与预测值的比对,得到均方差MSE与拟合优度,进而得到最优参数。最后通过大量的实验数据,验证了该模型在此次研究中取得了很好的预测结果。

基于ARMA模型的隧道变形预测及参数估计分析

以北京市海淀区某地铁站一体化棚户区改造项目为例,运用ARMA模型对高层建筑盖挖逆作法施工过程中邻近既有地铁隧道变形进行预测。以既有地铁隧道沉降实时监测数据为原始数据集,对原始数据集进行适当插补处理后,通过极大似然估计法对模型进行参数估计,给出了模型关键参数,构建了合理的预测模型。将模型预测结果与实测数据进行对比,显示预测结果与实测数据变化趋势高度吻合,充分验证了预测模型的可行性、有效性与稳定性。

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

Auto-Regressive Models of Non-Stationary Time Series with Finite Length

To analyze and simulate non-stationary time series with finite length, the statistical characteris- tics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and stud- ied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments re- garded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

Empirical analysis on risk of security investment

The paper analyzes the theory and application of Markowitz Mean-Variance Model and CAPM model. Firstly, it explains the development process and standpoints of two models and deduces the whole process in detail. Then 30 stocks are choosen from Shangzheng 50 stocks and are testified whether the prices of Shanghai stocks conform to the two models. With the technique of time series and panel data analysis, the research on the stock risk and effective portfolio by ORIGIN and MATLAB software is conducted. The result shows that Shanghai stock market conforms to Markowitz Mean-Variance Model to a certain extent and can give investors reliable suggestion to gain higher return, but there is no positive relation between system risk and profit ratio and CAPM doesn＇t function well in China＇s security market.

基于分位数回归的轨道质量指数阈值合理性数据分析

高速铁路轨道高平顺性是保障列车行车安全的重要基础,考虑到现有规范中针对轨道不平顺养护维修的局限性,需要对轨道动态不平顺管理值的合理性进行研究和改善。利用历史数据及统计方法挖掘轨道动态不平顺峰值管理值与均值管理值的联系,基于时间序列的异常检测与修正模型,通过分位数回归探究各项指标半峰值与标准差关系,并给出相应的均值管理建议值。结合7条不同工况线路的实测数据,研究结果表明:速度200~250 km/h线路建议采用80%及以上分位数的标准差作为管理值,而速度250(不含)~350 km/h线路建议采用95%及以上分位数的标准差作为管理值;相同时速线路、同一分位数下,若建议管理值越高,表明峰值与标准差的倍数较小,该线路状态较好,特别是单点不平顺超限的数量较少,不需要较严格的TQI管理值进行整体管理;反之,则表明单点超限可能较多,需要压低TQI管理值确保较低的峰值超限数量;对于5项不平顺指标,轨距出现峰值超限的概率最大,养护维修时需着重关注。