There is an increasing trend to apply GNSS continuous observation of short baselines to the monitoring of engineering works, such as bridges and dams, for their structural analysis and safety control. In the case of l...There is an increasing trend to apply GNSS continuous observation of short baselines to the monitoring of engineering works, such as bridges and dams, for their structural analysis and safety control. In the case of large dams, one important application of the GNSS continuous observation is thc establishment of early warning systems that demand accurate, frequently updated information and where the analysis of the baseline time series, in order to separate signal from noise is mandatory. The paper presents a study on the performance of linear filters of the asymmetric moving average type to smooth baseline time series. The transfer function of the filter is adopted as a smoothing criterion to choose an adequate order for the moving average, in face of the spectral density function of the baseline time series. Onc series of measurements of a short test baseline （325 m）, materialized in the campus of the National Laboratory for Civil Engineering, is used as an example of the proposed strategy.展开更多
Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+...Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.展开更多
Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world ove...Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.展开更多
The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector ...The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.展开更多
The m-widely orthant dependent(m-WOD)sequences are very weak dependent random variables.In the paper,the authors investigate the moving average processes,which is generated by m-WOD random variables.By using the tail ...The m-widely orthant dependent(m-WOD)sequences are very weak dependent random variables.In the paper,the authors investigate the moving average processes,which is generated by m-WOD random variables.By using the tail cut technique and maximum moment inequality of the m-WOD random variables,moment complete convergence and complete convergence of the maximal partial sums for the moving average processes are obtained,the results generalize and improve some corresponding results of the existing literature.展开更多
Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden ...Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.展开更多
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2....Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.展开更多
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 s...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.展开更多
In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling met...In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.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
A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive m...A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive moving average model （ARMA） to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i. e. , artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can ① effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, ② minimize the total communication consumption, and ③prolong the lifetime of networks.展开更多
In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis c...In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets.To implement this research,we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member.We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.Our assessment updated the findings of previous research by including more recent data and adding South Africa,the latest member included in BRICS.Our results showed that the returns obtained by the automated system,on average,exceeded the value invested.There were groups of assets from each country that performed well above the portfolio average,surpassing the returns obtained using a buy and hold strategy.The returns from the sample portfolio were very strong in Russia and India.We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.展开更多
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...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.展开更多
The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF...The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF)structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization.The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average(ARMA)model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators.(The sensors and actuators are required to maintain the identical number.)under any dimensional stationary stochastic excitation.展开更多
An important task of Internet congestion control is inhibiting sporadic data flow to maintain a suitable window size or route queue length. Such a requirement is just consistent with the basic idea and function of a m...An important task of Internet congestion control is inhibiting sporadic data flow to maintain a suitable window size or route queue length. Such a requirement is just consistent with the basic idea and function of a moving average filter. In this paper one prior Internet congestion control model, named transmission control protocol (TCP)/random early detection (RED) stroboscopic model, is studied, and then one new scheme is proposed to enlarge its stable domain, where a simple moving average filter is introduced to inhibit sporadic data flow as possible. In the novel scheme the bifurcation phenomenon is postponed without any extra controller. The effectiveness of the new scheme is verified by theoretical analyses and numerical simulations.展开更多
In this paper,we obtained complete qth-moment convergence of the moving average processes,which is generated by m-WOD moving random variables.The results in this article improve and extend the results of the moving av...In this paper,we obtained complete qth-moment convergence of the moving average processes,which is generated by m-WOD moving random variables.The results in this article improve and extend the results of the moving average process.mWOD random variables include WOD,m-NA,m-NOD and mEND random variables,so the results in the paper also promote the corresponding ones in WOD,m-NA,m-NOD,m-END random variables.展开更多
Cyclic variability is a factor adversely affecting engine performance. In this paper a cyclic moving average regulation approach to cylinder pressure at top dead center （TDC） is proposed, where the ignition time is ...Cyclic variability is a factor adversely affecting engine performance. In this paper a cyclic moving average regulation approach to cylinder pressure at top dead center （TDC） is proposed, where the ignition time is adopted as the control input. The dynamics from ignition time to the moving average index is described by ARMA model. With this model, a one-step ahead prediction-based minimum variance controller （MVC） is developed for regulation. The performance of the proposed controller is illustrated by experiments with a commercial car engine and experimental results show that the controller has a reliable effect on index regulation when the engine works under different fuel injection strategies, load changing and throttle opening disturbance.展开更多
An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency...An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.展开更多
The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are d...The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.展开更多
文摘There is an increasing trend to apply GNSS continuous observation of short baselines to the monitoring of engineering works, such as bridges and dams, for their structural analysis and safety control. In the case of large dams, one important application of the GNSS continuous observation is thc establishment of early warning systems that demand accurate, frequently updated information and where the analysis of the baseline time series, in order to separate signal from noise is mandatory. The paper presents a study on the performance of linear filters of the asymmetric moving average type to smooth baseline time series. The transfer function of the filter is adopted as a smoothing criterion to choose an adequate order for the moving average, in face of the spectral density function of the baseline time series. Onc series of measurements of a short test baseline （325 m）, materialized in the campus of the National Laboratory for Civil Engineering, is used as an example of the proposed strategy.
基金Supported by the Academic Achievement Re-cultivation Projects of Jingdezhen Ceramic University(Grant Nos.215/20506341215/20506277)the Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)。
文摘Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.
文摘Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.
文摘The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.
基金Supported by the Academic Funding Projects for Top Talents in Universities of Anhui Province (gxbjZD2022067, gxbjZD2021078)the Key Grant Project for Academic Leaders of Tongling University(2020tlxyxs31, 2020tlxyxs09)。
文摘The m-widely orthant dependent(m-WOD)sequences are very weak dependent random variables.In the paper,the authors investigate the moving average processes,which is generated by m-WOD random variables.By using the tail cut technique and maximum moment inequality of the m-WOD random variables,moment complete convergence and complete convergence of the maximal partial sums for the moving average processes are obtained,the results generalize and improve some corresponding results of the existing literature.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ30817)Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project(2025JJ70129)+1 种基金Changsha Natural Science Foundation(kq2403057)China。
文摘Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.
基金the Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
文摘Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.
基金Supported by the Key Scientific Research Project of Universities in Henan Province,No.21A330004Natural Science Foundation in Henan Province,No.222300420265.
文摘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.
基金supported by Science and Technology project of the State Grid Corporation of China“Research on Active Development Planning Technology and Comprehensive Benefit Analysis Method for Regional Smart Grid Comprehensive Demonstration Zone”National Natural Science Foundation of China(51607104)
文摘In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.
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.
基金Supported by the National Natural Science Foundation of China(60802005,60965002,50803016)Science Foundation forthe Excellent Youth Scholars at East China University of Science and Technology(YH0157127)Undergraduate Innovational Experimentation Program in ECUST(X1033)
文摘A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive moving average model （ARMA） to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i. e. , artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can ① effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, ② minimize the total communication consumption, and ③prolong the lifetime of networks.
文摘In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets.To implement this research,we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member.We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.Our assessment updated the findings of previous research by including more recent data and adding South Africa,the latest member included in BRICS.Our results showed that the returns obtained by the automated system,on average,exceeded the value invested.There were groups of assets from each country that performed well above the portfolio average,surpassing the returns obtained using a buy and hold strategy.The returns from the sample portfolio were very strong in Russia and India.We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.
文摘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.
基金The project supported by the National Natural Science Foundation of China(50278054)
文摘The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF)structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization.The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average(ARMA)model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators.(The sensors and actuators are required to maintain the identical number.)under any dimensional stationary stochastic excitation.
基金the National Natural Science Foundation of China (No. 70571017)the Research Foundation from Provincial Education Department of Zhejiang of China (No. 20070928)
文摘An important task of Internet congestion control is inhibiting sporadic data flow to maintain a suitable window size or route queue length. Such a requirement is just consistent with the basic idea and function of a moving average filter. In this paper one prior Internet congestion control model, named transmission control protocol (TCP)/random early detection (RED) stroboscopic model, is studied, and then one new scheme is proposed to enlarge its stable domain, where a simple moving average filter is introduced to inhibit sporadic data flow as possible. In the novel scheme the bifurcation phenomenon is postponed without any extra controller. The effectiveness of the new scheme is verified by theoretical analyses and numerical simulations.
基金Supported by the Academic Funding Projects for Top Talents in Universities of Anhui Province(gxbj ZD2022067,gxbj ZD2021078)the Philosophy and Social Sciences Planning Project of Anhui Province(AHSKY2018D98)。
文摘In this paper,we obtained complete qth-moment convergence of the moving average processes,which is generated by m-WOD moving random variables.The results in this article improve and extend the results of the moving average process.mWOD random variables include WOD,m-NA,m-NOD and mEND random variables,so the results in the paper also promote the corresponding ones in WOD,m-NA,m-NOD,m-END random variables.
文摘Cyclic variability is a factor adversely affecting engine performance. In this paper a cyclic moving average regulation approach to cylinder pressure at top dead center （TDC） is proposed, where the ignition time is adopted as the control input. The dynamics from ignition time to the moving average index is described by ARMA model. With this model, a one-step ahead prediction-based minimum variance controller （MVC） is developed for regulation. The performance of the proposed controller is illustrated by experiments with a commercial car engine and experimental results show that the controller has a reliable effect on index regulation when the engine works under different fuel injection strategies, load changing and throttle opening disturbance.
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFF0607504)。
文摘An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,JeddahThe authors,therefore,gratefully acknowledge the DSR technical and financial support.
文摘The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.
