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

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World Journal of Gastroenterology 2023年第42期29卷 5716-5727页

作者： Yong-Bin Wang Si-Yu Qing Zi-Yue Liang Chang Ma Yi-Chun Bai Chun-Jie Xu Department of Epidemiology and Health Statistics School of Public HealthXinxiang Medical UniversityXinxiang 453003Henan ProvinceChina Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology Institute of Medicinal BiotechnologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100010China

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 *** 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 *** 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 *** analysis and the Hodrick-Prescott method were employed to identify trends and *** 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 *** There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June ***,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 *** 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 *** predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during ***

关键词： Hepatitis Seasonal autoregressive fractionally integrated moving average Seasonal autoregressive integrated moving average Prediction Epidemic time series analysis

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Price Prediction of Seasonal Items Using time series analysis

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Computer Systems Science & Engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

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World Journal of Clinical Cases 2023年第29期11卷 6974-6983页

作者： Latchezar Tomov Lyubomir Chervenkov Dimitrina Georgieva Miteva Hristiana Batselova TsvetelinaVelikova Department of Informatics New Bulgarian UniversitySofia 1618Bulgaria Department of Diagnostic Imaging Medical University PlovdivPlovdiv 4000Bulgaria Department of Genetics Faculty of BiologySofia University"St.Kliment Ohridski"Sofia 1164Bulgaria Department of Epidemiology and Disaster Medicine Medical UniversityUniversity Hospital"St George"Plovdiv 4000Bulgaria Department of Medical Faculty Sofia UniversitySt.Kliment OhridskiSofia 1407Bulgaria

time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and *** is related to explaining past and current data based on various internal and external influences that may or may not have a causative *** is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal *** time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as *** applicability in forecasting comes from its better accuracy for short-term *** its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data *** predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,*** can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.

关键词： time series analysis Epidemiology COVID-19 Pandemic Auto-regressive integrated moving average Excess mortality Seroprevalence

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Using time series analysis to assess tidal effect on coastal groundwater level in Southern Laizhou Bay, China

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Journal of Groundwater Science and Engineering 2022年第3期10卷 292-301页

作者： She-ming Chen Hong-wei Liu Fu-tian Liu Jin-jie Miao Xu Guo Zhou Zhang Wan-jun Jiang Tianjin Center of China Geological Survey Ministry of Natural ResourcesTianjin 300170China

Sea water intrusion is an environmental problem cause by the irrational exploitation of coastal groundwater resources and has attracted the attention of many coastal *** this study,we used time series monitoring data of groundwater levels and tidal waves to analyze the influence of tide flow on groundwater dynamics in the southern Laizhou *** auto-correlation and cross-correlation coefficients between groundwater level and tidal wave level were calculated specifically to measure the boundary conditions along the *** addition,spectrum analysis was employed to assess the periodicity and hysteresis of various tide and groundwater level *** results of time series analysis show that groundwater level fluctuation is noticeably influenced by tides,but the influence is limited to a certain distance and cannot reach the saltwater-freshwater interface in the southern Laizhou *** are three main periodic components of groundwater level in tidal effect range(i.e.23.804 h,12.500 h and 12.046 h),the pattern of which is the same as the *** affected groundwater level fluctuations lag behind the *** dynamic analysis of groundwater indicates that the coastal aquifer has a hydraulic connection with seawater but not in a direct *** to the existence of the groundwater mound between the salty groundwater(brine)and fresh groundwater,the maximum influencing distance of the tide on the groundwater is 8.85 *** that the fresh-saline groundwater interface is about 30 km away from the coastline,modern seawater has a limited contribution to sea-salt water intrusion in Laizhou *** results of this study are expected to provide a reference for the study on sea water intrusion.

关键词： Groundwater time series analysis Correlation Spectral analysis Sea-salt water intrusion

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time series analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

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Open Journal of Statistics 2023年第2期13卷 222-232页

作者： Lei Wang College of Science and Mathematics Augusta University Augusta USA

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.

关键词： Dynamic Harmonic Regression with ARIMA Errors COVID-19 Pandemic Forecasting Models time series analysis Weekly Seasonality

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Research on intelligent fault diagnosis based on time series analysis algorithm

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The Journal of China Universities of Posts and Telecommunications 2008年第1期15卷 68-74页

作者： CHEN Gang LIU Yang ZHOU Wen-an SONG Jun-de School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

Aiming to realize fast and accurate fault diagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent fault diagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio （GLR） test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced. Secondly, the intelligent fault diagnosis model is established by introducing neural network technology, and thereby, the anomaly information of each node in end-to-end network is integrated and processed in parallel to intelligently diagnose the fault cause. Finally, server backup solution in enterprise information network is taken as the simulation scenario. The results demonstrate that the proposed method can not only detect fault occurrence in time, but can also implement online diagnosis for fault cause, and thus, real-time and intelligent fault management process is achieved.

关键词： network management fault diagnosis time series analysis neural network

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STUDY OF MACHINING PROCESS MONITORING OF FMS BASED ON time series analysis

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Chinese Journal of Mechanical Engineering 2004年第1期17卷 121-123页

作者： ZhangLibin SuJian LiuYumei JiaYazhou CollegeofTransportation JilinUniversityChangchun130025China CollegeofMechanicalscienceandEngineering Changchun130025China

FMS is a sort of highly automatic machining system, how to ensure partquality is master key to system highly active running. At first, series of machining dimension andprocess capability of flexible manufacturing system(FMS), is analyzed. Result of its, strongself-correlation of data series shows that time series analysis is applicable to data seriesanalyzed. Based on-line modeling and forecasting for data series, principle and method of feedbackcompensation control is proposed. On a foundation of the virtual instrument platform, Labview ofnational instrument (NI), FMS dimension and process capability monitoring system(monitoring system)is developed. In practice, it is proved that part quality and process capability of FMS are greatlyimproved.

关键词： Dimension series time series analysis

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Feature extraction and damage alarming using time series analysis

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Journal of Southeast University(English Edition) 2007年第1期23卷 86-91页

作者：刘毅李爱群费庆国丁幼亮东南大学混凝土及预应力混凝土结构教育部重点实验室南京210096

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

关键词： feature extraction damage alarming time series analysis structural health monitoring

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

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Journal of Southeast University(English Edition) 2013年第2期29卷 170-174页

作者：陈伟李旭张为公东南大学仪器科学与工程学院南京210096

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.

关键词： microelectromechanical system (MEMS)gyroscope autoregressive integrated moving average(ARIMA) model time series analysis gross errors

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Hydrodynamic characteristics of a typical karst spring system based on time series analysis in northern China

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China Geology 2021年第3期4卷 433-445页

作者： Yi Guo Feng Wang Da-jun Qin Zhan-feng Zhao Fu-ping Gan Bai-kun Yan Juan Bai Haji Muhammed China Aero Geophysical Survey and Remote Sensing Center for Natural Resources China Geological SurveyBeijing 100083China Chinese Academy of Environmental Planning Beijing 100012China Key Laboratory of Shale Gas and Geoengineering Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China School of Applied Natural Science Adama Science and Technology UniversityP.O.Box 1888AdamaEthiopia

In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern *** show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,*** regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu *** auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological *** delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological *** addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are *** these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.

关键词： Karst spring Karst aquifer Hydrodynamic time series analysis Correlation analysis Spectral analysis Hydrogeological survey engineering Jinan Shandong Province China

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