An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi,Pakistan

AIM： To identify the stochastic autoregressive integrated moving average （ARIMA） model for short term forecasting of hepatitis C virus （HCV） seropositivity among volunteer blood donors in Karachi, Pakistan.METHODS： Ninety-six months （1998-2005） data on HCV seropositive cases （1000-1 x month1） among male volunteer blood donors tested at four major blood banks in Karachi, Pakistan were subjected to ARIMA modeling. Subsequently, a fitted ARIMA model was used to forecast HCV seropositive donors for 91-96 mo to contrast with observed series of the same months. To assess the forecast accuracy, the mean absolute error rate （%） between the observed and predicted HCV seroprevalence was calculated. Finally, a fitted ARIMA model was used for short-term forecasts beyond the observed series.RESULTS： The goodness-of-fit test of the optimum ARIMA （2,1,7） model showed non-significant autocorrelations in the residuals of the model. The forecasts by ARIMA for 91-96 mo closely followed the pattern of observed series for the same months, with mean monthly absolute forecast errors （%） over 6 mo of 6.5%. The short-term forecasts beyond the observed series adequately captured the pattern in the data and showed increasing tendency of HCV seropositivity with a mean ± SD HCV seroprevalence （1000^-1× month^-1） of 24.3 ±1.4 over the forecast interval.CONCLUSION： To curtail HCV spread, public health authorities need to educate communities and health care providers about HCV transmission routes based on known HCV epidemiology in Pakistan and its neighboring countries. Future research may focus on factors associated with hyperendemic levels of HCV infection.

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

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

Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region,China

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Effect of aggregation interval on vehicular traffic flow heteroscedasticity

The effect of the aggregation interval on vehicular traffic flow heteroscedasticity is investigated using real-world traffic flow data collected from the motorway system in the United Kingdom. 30 traffic flow series are generated using 30 aggregation intervals ranging from 1 to 30 min at 1 min increment, and autoregressive integrated moving average （AR/MA） models are constructed and applied in these series, generating 30 residual series. Through applying the portmanteau Q-test and the Lagrange multiplier （LM） test in the residual series from the ARIMA models, the heteroscedasticity in traffic flow series is investigated. Empirical results show that traffic flow is heteroscedastJc across these selected aggregation intervals, and longer aggregation intervals tend to cancel out the noise in the traffic flow data and hence reduce the heteroscedasticity in traffic flow series. The above findings can be utilized in the development of reliable and robust traffic management and control systems.

Time-varying confidence interval forecasting of travel time for urban arterials using ARIMA-GARCH model

To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity （ARIMA-GARCH） model. In which, the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time. The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that, compared with the conventional ARIMA model, the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.

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.

Forecasting the Monthly Reported Cases of Human Immunodeficiency Virus (HIV) at Minna Niger State, Nigeria

There has been a moderate increase in newly diagnosed HIV-infected Minna populace, which calls for serious attention. This study used time series data based on monthly HIV cases from January 2007 to December 2018 taken from the statistical data document on HIV prevalence recorded in General Hospital Minna, Niger State. The methodology employed to analyze the data is based on mathematical models of ARMA, ARIMA and SARIMA which were computed and diagnosed. From the results of parameter estimation of the models, ARMA(2, 1) model was the best model among the other ARMA models using information criteria (AIC). Diagnostic test was run on the ARMA(2, 1) model where the results show that the model was adequate and normally distributed using Box-Lung test and Q-Q plot respectively. Furthermore, ARIMA of first and second differences was estimated and ARIMA(1, 0, 1) was the best model from the result of the AIC and diagnostic test carried out which revealed that the model was adequate and normally distributed using Box-Lung and Q-Q plot respectively. Furthermore, the results obtained in the ARMA and ARIMA models were used to arrive at a combined model given as ARIMA(1, 0, 1) × SARIMA(1, 0, 1)12 which was subsequently estimated and found to be adequate from the result of the Box-Lung and Q-Q plot respectively. Post forecasting estimation and performance evolution were evaluated using the RMSE and MAE. The results showed that, ARIMA(1, 0, 1) × SARIMA(1, 0, 1)12 is the best forecasting model followed by ARIMA(1, 0, 2) on monthly HIV prevalence in Minna, Niger state.

天气衍生品气温预测模型对比研究

气温衍生品是天气衍生品交易中最活跃的合约之一,确定合理预测气温动态变化的模型,是气温衍生品开发设计的基础。考虑到气温在时间变化上具有趋势性、季节性和周期性等特点,文中使用了以O-U均值回复过程为基础的Continuous Time Autoregressive Model(CAR)模型、Seasonal Autoregressive Integrated Moving Average(SARIMA)模型和小波神经网络算法,并选择漠河、北京、乌鲁木齐、芜湖、昆明和海口具有地域性代表的城市气温进行拟合,使用无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差检验指标检验了模型的预测精度。研究结果表明,小波神经网络算法在预测6个城市的无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差的值最小;同时,相比CAR模型、SARIMA模型,其预测效果最优。因此,小波神经网络算法能够很好地拟合气温数据的变化,可以为我国气温天气衍生品的定价提供一定的指导。

Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data.Methods: Timeseries analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015.Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied.Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables.The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe.It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined.Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models.This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.

Performance evaluation of series and parallel strategies for financial time series forecasting

Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied approach.In general,individual models are combined under two main strategies:series and parallel.While it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.Methods:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.Results:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price.To do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm models.Conclusion:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen Integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.

Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.

An Early Stage Detecting Method against SYN Flooding Attacks

Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple but effective method to detect SYN flooding attacks at the early stage.In the ESDM the SYN traffic is forecasted by autoregressive integrated moving average model, and non-parametric cumulative sum algorithm is used to find the SYN flooding attacks according to the forecasted traffic.Trace-driven simulations show that ESDM is accurate and efficient to detect the SYN flooding attacks.

云南省总人口预测模型的比较研究

本文以1973~2018年云南省总人口为例,分别建立Holt两参数指数平滑模型、ARIMA模型和三次多项式模型,利用最小AIC准则从ARIMA模型中选出了ARIMA(4,3,1)模型,与Holt两参数指数平滑模型和三次多项式模型做比较。通过模型预测值的平均误差率和残差的波动幅度的比较后,发现ARIMA(4,3,1)模型的拟合精度较高,适合用来预测短期的总人口数。基于此分析对云南省总人口进行了8期数的预测,发现云南省总人口数量呈现不断增加的趋势,但总人口数增长速率下降,总人口数量趋向饱和状态。

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interruption,and other factors,a method of anomaly recognition and differentiation for monitoring data was proposed.Firstly,the empirical wavelet transform(EWT)and the autoregressive integrated moving average(ARIMA)model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value,and then the isolation forest algorithm was used to identify the abnormal information,and the monitoring sequence was segmented according to the recognition results.Secondly,the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method,and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors,and the monitoring sequence correlation was further used to verify the assessment.Finally,the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

Predicting Electric Energy Consumption for a Jerky Enterprise

Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was used to make day, week and month ahead prediction. The prediction effect of prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more efficiently. However, for mid- range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.

A Research on Demands Forecasting and Personnel Training of Tourism Talents A Case Study of Zhejiang Province

By analyzing the recent 15 years＇ statistical data of Zhejiang tourism human resources, this paper analyzes the status of Zhejiang tourism talents. ARIMA （Autoregressive Integrated Moving Average） model is a method of time series prediction. This paper predicts the trends of the next three years＇ demands of Zhejiang tourism talents based on ARIMA model in order to promote the tourism in Zhejiang Province. According to the demands forecasting, the number of the employees required by the hotels is 10 times of travel agencies in 2015. At last, some solutions and suggestions are provided such as strengthening the talents training establishing tourism talents mobility mechanism and improving tourism talents excitation mechanism

Modeling and forecasting time series of precious metals:a new approach to multifractal data

We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.

A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

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

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